diff --git a/brax/training/agents/td3/__init__.py b/brax/training/agents/td3/__init__.py
new file mode 100644
index 000000000..56d0ba60f
--- /dev/null
+++ b/brax/training/agents/td3/__init__.py
@@ -0,0 +1,14 @@
+# Copyright 2024 The Brax Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
diff --git a/brax/training/agents/td3/losses.py b/brax/training/agents/td3/losses.py
new file mode 100644
index 000000000..e984f5cb9
--- /dev/null
+++ b/brax/training/agents/td3/losses.py
@@ -0,0 +1,71 @@
+"""Twin Delayed Deep Deterministic Policy Gradient (TD3) losses.
+
+See: https://arxiv.org/pdf/1802.09477.pdf
+"""
+from typing import Any
+
+import jax
+import jax.numpy as jnp
+from brax.training import types
+from brax.training.types import Params
+from brax.training.types import PRNGKey
+from brax.training.agents.td3 import networks as td3_networks
+
+Transition = types.Transition
+
+
+def make_losses(
+ td3_network: td3_networks.TD3Networks,
+ reward_scaling: float,
+ discounting: float,
+ smoothing: float,
+ noise_clip: float,
+ max_action: float = 1.0,
+ bc: bool = False,
+ alpha: float = 2.5):
+ """Creates the TD3 losses."""
+ policy_network = td3_network.policy_network
+ q_network = td3_network.q_network
+
+ def critic_loss(
+ q_params: Params,
+ target_q_params: Params,
+ target_policy_params: Params,
+ normalizer_params: Any,
+ transitions: Transition,
+ key: PRNGKey) -> jnp.ndarray:
+ """Calculates the TD3 critic loss."""
+
+ current_q1_q2 = q_network.apply(normalizer_params, q_params, transitions.observation, transitions.action)
+ next_actions = policy_network.apply(normalizer_params, target_policy_params, transitions.next_observation)
+ smoothing_noise = (jax.random.normal(key, next_actions.shape) * smoothing).clip(-noise_clip, noise_clip)
+ next_actions = (next_actions + smoothing_noise).clip(-max_action, max_action)
+
+ next_q1_q2 = q_network.apply(normalizer_params, target_q_params, transitions.next_observation, next_actions)
+ target_q = jnp.min(next_q1_q2, axis=-1)
+ target_q = jax.lax.stop_gradient(
+ transitions.reward * reward_scaling + transitions.discount * discounting * target_q)
+
+ q_error = current_q1_q2 - jnp.expand_dims(target_q, -1)
+ q_loss = 0.5 * jnp.mean(jnp.square(q_error))
+
+ return q_loss
+
+ def actor_loss(
+ policy_params: Params,
+ q_params: Params,
+ normalizer_params: Any,
+ transitions: Transition) -> jnp.ndarray:
+ """Calculates the TD3 actor loss."""
+
+ new_actions = policy_network.apply(normalizer_params, policy_params, transitions.observation)
+ q_new_actions = q_network.apply(normalizer_params, q_params, transitions.observation, new_actions)
+ q_new_actions, _ = jnp.split(q_new_actions, 2, axis=-1)
+ lmbda = jax.lax.stop_gradient(bc * alpha / jnp.mean(jnp.abs(q_new_actions)) + (1 - bc))
+ q_mean = jnp.mean(q_new_actions)
+ return - lmbda * q_mean + bc * mean_squared_error(new_actions, transitions.action)
+
+ def mean_squared_error(predictions, targets):
+ return jnp.mean(jnp.square(predictions - targets))
+
+ return critic_loss, actor_loss
diff --git a/brax/training/agents/td3/networks.py b/brax/training/agents/td3/networks.py
new file mode 100644
index 000000000..46870f45b
--- /dev/null
+++ b/brax/training/agents/td3/networks.py
@@ -0,0 +1,88 @@
+"""TD3 networks."""
+
+from typing import Sequence, Tuple
+
+import jax.numpy as jnp
+from brax.training import networks
+from brax.training import types
+from brax.training.networks import ActivationFn, FeedForwardNetwork, Initializer, MLP
+from brax.training.types import PRNGKey
+from flax import linen, struct
+import jax
+
+
+@struct.dataclass
+class TD3Networks:
+ policy_network: networks.FeedForwardNetwork
+ q_network: networks.FeedForwardNetwork
+
+
+def make_inference_fn(td3_networks: TD3Networks):
+ """Creates params and inference function for the TD3 agent."""
+
+ def make_policy(params: types.PolicyParams, exploration_noise, noise_clip) -> types.Policy:
+ def policy(observations: types.Observation,
+ key_noise: PRNGKey) -> Tuple[types.Action, types.Extra]:
+ actions = td3_networks.policy_network.apply(*params, observations)
+ noise = (jax.random.normal(key_noise, actions.shape) * exploration_noise).clip(-noise_clip, noise_clip)
+ return actions + noise, {}
+
+ return policy
+
+ return make_policy
+
+
+def make_policy_network(
+ param_size: int,
+ obs_size: int,
+ preprocess_observations_fn: types.PreprocessObservationFn = types
+ .identity_observation_preprocessor,
+ hidden_layer_sizes: Sequence[int] = (256, 256),
+ activation: ActivationFn = linen.relu,
+ kernel_init: Initializer = jax.nn.initializers.lecun_uniform(),
+ layer_norm: bool = False) -> FeedForwardNetwork:
+ """Creates a policy network."""
+ policy_module = MLP(
+ layer_sizes=list(hidden_layer_sizes) + [param_size],
+ activation=activation,
+ kernel_init=kernel_init,
+ layer_norm=layer_norm)
+
+ def apply(processor_params, policy_params, obs):
+ obs = preprocess_observations_fn(obs, processor_params)
+ raw_actions = policy_module.apply(policy_params, obs)
+ return linen.tanh(raw_actions)
+
+ dummy_obs = jnp.zeros((1, obs_size))
+ return FeedForwardNetwork(init=lambda key: policy_module.init(key, dummy_obs), apply=apply)
+
+
+def make_td3_networks(
+ observation_size: int,
+ action_size: int,
+ preprocess_observations_fn: types.PreprocessObservationFn = types
+ .identity_observation_preprocessor,
+ hidden_layer_sizes: Sequence[int] = (256, 256),
+ activation: networks.ActivationFn = linen.relu) -> TD3Networks:
+ """Make TD3 networks."""
+ policy_network = make_policy_network(
+ action_size,
+ observation_size,
+ preprocess_observations_fn=preprocess_observations_fn,
+ hidden_layer_sizes=hidden_layer_sizes,
+ activation=activation,
+ )
+
+ q_network = networks.make_q_network(
+ observation_size,
+ action_size,
+ preprocess_observations_fn=preprocess_observations_fn,
+ hidden_layer_sizes=hidden_layer_sizes,
+ activation=activation)
+
+ return TD3Networks(
+ policy_network=policy_network,
+ q_network=q_network)
+
+
+
diff --git a/brax/training/agents/td3/train.py b/brax/training/agents/td3/train.py
new file mode 100644
index 000000000..4ebdca996
--- /dev/null
+++ b/brax/training/agents/td3/train.py
@@ -0,0 +1,587 @@
+# Copyright 2024 The Brax Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""TD3 training.
+
+See: https://arxiv.org/pdf/1802.09477.pdf
+"""
+
+import functools
+import time
+from copy import deepcopy
+from typing import Any, Callable, Optional, Tuple, Union, Sequence
+
+import jax
+import jax.numpy as jnp
+import optax
+from absl import logging
+from brax import base
+from brax import envs
+from brax.io import model
+from brax.training import acting
+from brax.training import pmap
+from brax.training import replay_buffers
+from brax.training import types
+from brax.training.acme import running_statistics
+from brax.training.acme import specs
+from brax.training.gradients import gradient_update_fn
+from brax.training.types import PRNGKey
+from brax.training.types import Params
+from brax.v1 import envs as envs_v1
+from flax import struct
+
+from brax.training.agents.td3 import losses as td3_losses
+from brax.training.agents.td3 import networks as td3_networks
+
+Metrics = types.Metrics
+Transition = types.Transition
+InferenceParams = Tuple[running_statistics.NestedMeanStd, Params]
+
+ReplayBufferState = Any
+
+_PMAP_AXIS_NAME = "i"
+
+
+@struct.dataclass
+class TrainingState:
+ """Contains training state for the learner."""
+
+ policy_params: Params
+ target_policy_params: Params
+ policy_optimizer_state: optax.OptState
+ q_params: Params
+ target_q_params: Params
+ q_optimizer_state: optax.OptState
+ gradient_steps: jnp.ndarray
+ env_steps: jnp.ndarray
+ normalizer_params: running_statistics.RunningStatisticsState
+
+
+def _unpmap(v):
+ return jax.tree_util.tree_map(lambda x: x[0], v)
+
+
+def soft_update(target_params: Params, online_params: Params, tau) -> Params:
+ return jax.tree_util.tree_map(
+ lambda x, y: (1 - tau) * x + tau * y, target_params, online_params
+ )
+
+
+def _init_training_state(
+ key: PRNGKey,
+ obs_size: int,
+ local_devices_to_use: int,
+ td3_network: td3_networks.TD3Networks,
+ policy_optimizer: optax.GradientTransformation,
+ q_optimizer: optax.GradientTransformation,
+) -> TrainingState:
+ """Inits the training state and replicates it over devices."""
+ key_policy, key_q = jax.random.split(key)
+
+ policy_params = td3_network.policy_network.init(key_policy)
+ policy_optimizer_state = policy_optimizer.init(policy_params)
+ q_params = td3_network.q_network.init(key_q)
+ q_optimizer_state = q_optimizer.init(q_params)
+
+ normalizer_params = running_statistics.init_state(
+ specs.Array((obs_size,), jnp.dtype("float32"))
+ )
+
+ training_state = TrainingState(
+ policy_optimizer_state=policy_optimizer_state,
+ policy_params=policy_params,
+ target_policy_params=deepcopy(policy_params),
+ q_optimizer_state=q_optimizer_state,
+ q_params=q_params,
+ target_q_params=deepcopy(q_params),
+ gradient_steps=jnp.zeros(()),
+ env_steps=jnp.zeros(()),
+ normalizer_params=normalizer_params,
+ )
+ return jax.device_put_replicated(
+ training_state, jax.local_devices()[:local_devices_to_use]
+ )
+
+
+def train(
+ environment: Union[envs.Env, envs_v1.Env],
+ num_timesteps,
+ episode_length: int,
+ action_repeat: int = 1,
+ num_envs: int = 1,
+ num_eval_envs: int = 128,
+ learning_rate: float = 1e-4,
+ adam_eps: float = 1e-5,
+ discounting: float = 0.99,
+ seed: int = 0,
+ batch_size: int = 256,
+ num_evals: int = 1,
+ normalize_observations: bool = False,
+ max_devices_per_host: Optional[int] = None,
+ reward_scaling: float = 1.0,
+ tau: float = 0.005,
+ min_replay_size: int = 0,
+ max_replay_size: Optional[int] = None,
+ grad_updates_per_step: int = 1,
+ policy_delay: int = 2,
+ smoothing_noise: int = 0.2,
+ exploration_noise: float = 0.2,
+ noise_clip: float = 0.5,
+ bc: bool = False,
+ alpha: float = 2.5,
+ network_factory: types.NetworkFactory[
+ td3_networks.TD3Networks
+ ] = td3_networks.make_td3_networks,
+ hidden_layer_sizes: Sequence[int] = (256, 256),
+ progress_fn: Callable[[int, Metrics], None] = lambda *args: None,
+ checkpoint_logdir: Optional[str] = None,
+ eval_env: Optional[envs.Env] = None,
+ randomization_fn: Optional[
+ Callable[[base.System, jnp.ndarray], Tuple[base.System, base.System]]
+ ] = None
+):
+ """TD3 training."""
+ process_id = jax.process_index()
+ local_devices_to_use = jax.local_device_count()
+ if max_devices_per_host is not None:
+ local_devices_to_use = min(local_devices_to_use, max_devices_per_host)
+ device_count = local_devices_to_use * jax.process_count()
+ logging.info(
+ "local_device_count: %s; total_device_count: %s",
+ local_devices_to_use,
+ device_count,
+ )
+
+ if min_replay_size >= num_timesteps:
+ raise ValueError(
+ "No training will happen because min_replay_size >= num_timesteps"
+ )
+
+ if max_replay_size is None:
+ max_replay_size = num_timesteps
+
+ # The number of environment steps executed for every `critic_step()` call. (update every)
+ env_steps_per_actor_step = action_repeat * num_envs
+ # equals to ceil(min_replay_size / env_steps_per_actor_step)
+ num_prefill_actor_steps = -(-min_replay_size // num_envs)
+ num_prefill_env_steps = num_prefill_actor_steps * env_steps_per_actor_step
+ assert num_timesteps - num_prefill_env_steps >= 0
+ num_evals_after_init = max(num_evals - 1, 1)
+ # The number of run_one_td3_epoch calls per run_td3_training.
+ # equals to
+ # ceil(num_timesteps - num_prefill_env_steps /
+ # (num_evals_after_init * env_steps_per_actor_step))
+ num_training_steps_per_epoch = -(
+ -(num_timesteps - num_prefill_env_steps)
+ // (num_evals_after_init * env_steps_per_actor_step)
+ )
+
+ assert num_envs % device_count == 0
+ env = environment
+ if isinstance(env, envs.Env):
+ wrap_for_training = envs.training.wrap
+ else:
+ wrap_for_training = envs_v1.wrappers.wrap_for_training
+
+ rng = jax.random.PRNGKey(seed)
+ rng, key = jax.random.split(rng)
+ v_randomization_fn = None
+ if randomization_fn is not None:
+ v_randomization_fn = functools.partial(
+ randomization_fn,
+ rng=jax.random.split(
+ key, num_envs // jax.process_count() // local_devices_to_use
+ ),
+ )
+ env = wrap_for_training(
+ env,
+ episode_length=episode_length,
+ action_repeat=action_repeat,
+ randomization_fn=v_randomization_fn,
+ )
+
+ obs_size = env.observation_size
+ action_size = env.action_size
+
+ normalize_fn = lambda x, y: x
+ if normalize_observations:
+ normalize_fn = running_statistics.normalize
+ td3_network = network_factory(
+ observation_size=obs_size,
+ action_size=action_size,
+ preprocess_observations_fn=normalize_fn,
+ hidden_layer_sizes=hidden_layer_sizes
+ )
+ make_policy = td3_networks.make_inference_fn(td3_network)
+
+ policy_optimizer = optax.adam(learning_rate=learning_rate, eps=adam_eps)
+ q_optimizer = optax.adam(learning_rate=learning_rate, eps=adam_eps)
+
+ dummy_obs = jnp.zeros((obs_size,))
+ dummy_action = jnp.zeros((action_size,))
+ dummy_transition = Transition(
+ observation=dummy_obs,
+ action=dummy_action,
+ reward=jnp.array(0.),
+ discount=jnp.array(0.),
+ next_observation=dummy_obs,
+ extras={"state_extras": {"truncation": 0.0}, "policy_extras": {}},
+ )
+ replay_buffer = replay_buffers.UniformSamplingQueue(
+ max_replay_size=max_replay_size // device_count,
+ dummy_data_sample=dummy_transition,
+ sample_batch_size=batch_size * grad_updates_per_step // device_count,
+ )
+
+ critic_loss_fn, actor_loss_fn = td3_losses.make_losses(
+ td3_network=td3_network,
+ reward_scaling=reward_scaling,
+ discounting=discounting,
+ smoothing=smoothing_noise,
+ noise_clip=noise_clip,
+ bc=bc,
+ alpha=alpha,
+ )
+ critic_update = gradient_update_fn(
+ critic_loss_fn, q_optimizer, pmap_axis_name=_PMAP_AXIS_NAME
+ )
+ actor_update = gradient_update_fn(
+ actor_loss_fn, policy_optimizer, pmap_axis_name=_PMAP_AXIS_NAME
+ )
+
+ def sgd_step(
+ carry: Tuple[TrainingState, PRNGKey],
+ transitions: Transition
+ ) -> Tuple[Tuple[TrainingState, PRNGKey], Metrics]:
+ training_state, key = carry
+
+ key, key_critic = jax.random.split(key, 2)
+
+ critic_loss, q_params, q_optimizer_state = critic_update(
+ training_state.q_params,
+ training_state.target_q_params,
+ training_state.target_policy_params,
+ training_state.normalizer_params,
+ transitions,
+ key_critic,
+ optimizer_state=training_state.q_optimizer_state,
+ )
+
+ def dont_policy_update(training_state):
+ return (0.0, training_state.policy_params, training_state.policy_optimizer_state,
+ training_state.target_q_params, training_state.target_policy_params)
+
+ def do_policy_update(training_state):
+ actor_loss, policy_params, policy_optimizer_state = actor_update(
+ training_state.policy_params,
+ training_state.q_params,
+ training_state.normalizer_params,
+ transitions,
+ optimizer_state=training_state.policy_optimizer_state,
+ )
+ new_target_q_params = soft_update(training_state.target_q_params, q_params, tau)
+ new_target_policy_params = soft_update(training_state.policy_params, policy_params, tau)
+ return (actor_loss, policy_params, policy_optimizer_state,
+ new_target_q_params, new_target_policy_params)
+
+ update_policy = training_state.gradient_steps % policy_delay == 0
+ (actor_loss, policy_params,
+ policy_optimizer_state, new_target_q_params, new_target_policy_params)\
+ = jax.lax.cond(update_policy, do_policy_update, dont_policy_update, training_state)
+ metrics = {
+ "critic_loss": critic_loss,
+ "actor_loss": actor_loss,
+ }
+
+ new_training_state = TrainingState(
+ policy_optimizer_state=policy_optimizer_state,
+ policy_params=policy_params,
+ target_policy_params=new_target_policy_params,
+ q_optimizer_state=q_optimizer_state,
+ q_params=q_params,
+ target_q_params=new_target_q_params,
+ gradient_steps=training_state.gradient_steps + 1,
+ env_steps=training_state.env_steps,
+ normalizer_params=training_state.normalizer_params,
+ )
+ return (new_training_state, key), metrics
+
+ def get_experience(
+ normalizer_params: running_statistics.RunningStatisticsState,
+ policy_params: Params,
+ env_state: Union[envs.State, envs_v1.State],
+ buffer_state: ReplayBufferState,
+ key: PRNGKey,
+ ) -> Tuple[
+ running_statistics.RunningStatisticsState,
+ Union[envs.State, envs_v1.State],
+ ReplayBufferState,
+ ]:
+ policy = make_policy((normalizer_params, policy_params), exploration_noise=exploration_noise,
+ noise_clip=noise_clip)
+ env_state, transitions = acting.actor_step(
+ env, env_state, policy, key, extra_fields=("truncation",)
+ )
+
+ normalizer_params = running_statistics.update(
+ normalizer_params, transitions.observation, pmap_axis_name=_PMAP_AXIS_NAME
+ )
+
+ buffer_state = replay_buffer.insert(buffer_state, transitions)
+ return normalizer_params, env_state, buffer_state
+
+ def training_step(
+ training_state: TrainingState,
+ env_state: envs.State,
+ buffer_state: ReplayBufferState,
+ key: PRNGKey,
+ ) -> Tuple[
+ TrainingState, Union[envs.State, envs_v1.State],
+ ReplayBufferState, Metrics]:
+ experience_key, training_key = jax.random.split(key)
+ normalizer_params, env_state, buffer_state = get_experience(
+ training_state.normalizer_params,
+ training_state.policy_params,
+ env_state,
+ buffer_state,
+ experience_key,
+ )
+ training_state = training_state.replace(
+ normalizer_params=normalizer_params,
+ env_steps=training_state.env_steps + env_steps_per_actor_step,
+ )
+
+ buffer_state, transitions = replay_buffer.sample(buffer_state)
+ # Change the front dimension of transitions so 'update_step' is called
+ # grad_updates_per_step times by the scan.
+ transitions = jax.tree_util.tree_map(
+ lambda x: jnp.reshape(x, (grad_updates_per_step, -1) + x.shape[1:]),
+ transitions,
+ )
+ (training_state, _), metrics = jax.lax.scan(
+ sgd_step, (training_state, training_key), transitions
+ )
+
+ metrics["buffer_current_size"] = replay_buffer.size(buffer_state)
+ return training_state, env_state, buffer_state, metrics
+
+ def prefill_replay_buffer(
+ training_state: TrainingState,
+ env_state: envs.State,
+ buffer_state: ReplayBufferState,
+ key: PRNGKey,
+ ) -> Tuple[TrainingState, envs.State, ReplayBufferState, PRNGKey]:
+ def f(carry, unused):
+ del unused
+ training_state, env_state, buffer_state, key = carry
+ key, new_key = jax.random.split(key)
+ new_normalizer_params, env_state, buffer_state = get_experience(
+ training_state.normalizer_params,
+ training_state.policy_params,
+ env_state,
+ buffer_state,
+ key,
+ )
+ new_training_state = training_state.replace(
+ normalizer_params=new_normalizer_params,
+ env_steps=training_state.env_steps + env_steps_per_actor_step,
+ )
+ return (new_training_state, env_state, buffer_state, new_key), ()
+
+ return jax.lax.scan(
+ f,
+ (training_state, env_state, buffer_state, key),
+ (),
+ length=num_prefill_actor_steps,
+ )[0]
+
+ prefill_replay_buffer = jax.pmap(prefill_replay_buffer, axis_name=_PMAP_AXIS_NAME)
+
+ def training_epoch(
+ training_state: TrainingState,
+ env_state: envs.State,
+ buffer_state: ReplayBufferState,
+ key: PRNGKey,
+ ) -> Tuple[TrainingState, envs.State, ReplayBufferState, Metrics]:
+ def f(carry, unused_t):
+ ts, es, bs, k = carry
+ k, new_key = jax.random.split(k)
+ ts, es, bs, metrics = training_step(ts, es, bs, k)
+ return (ts, es, bs, new_key), metrics
+
+ (training_state, env_state, buffer_state, key), metrics = jax.lax.scan(
+ f,
+ (training_state, env_state, buffer_state, key),
+ (),
+ length=num_training_steps_per_epoch,
+ )
+ metrics = jax.tree_util.tree_map(jnp.mean, metrics)
+ return training_state, env_state, buffer_state, metrics
+
+ training_epoch = jax.pmap(training_epoch, axis_name=_PMAP_AXIS_NAME)
+
+ # Note that this is NOT a pure jittable method.
+ def training_epoch_with_timing(
+ training_state: TrainingState,
+ env_state: envs.State,
+ buffer_state: ReplayBufferState,
+ key: PRNGKey,
+ ) -> Tuple[TrainingState, envs.State, ReplayBufferState, Metrics]:
+ nonlocal training_walltime
+ t = time.time()
+ (training_state, env_state, buffer_state, metrics) = training_epoch(
+ training_state, env_state, buffer_state, key
+ )
+ metrics = jax.tree_util.tree_map(jnp.mean, metrics)
+ jax.tree_util.tree_map(lambda x: x.block_until_ready(), metrics)
+
+ epoch_training_time = time.time() - t
+ training_walltime += epoch_training_time
+ sps = (
+ env_steps_per_actor_step * num_training_steps_per_epoch
+ ) / epoch_training_time
+ metrics = {
+ "training/sps": sps,
+ "training/walltime": training_walltime,
+ **{f"training/{name}": value for name, value in metrics.items()},
+ }
+ return (
+ training_state,
+ env_state,
+ buffer_state,
+ metrics,
+ ) # pytype: disable=bad-return-type # py311-upgrade
+
+ def save_model(pmapped_params, model_name):
+ params = _unpmap(pmapped_params)
+ path = f"{checkpoint_logdir}/{model_name}.pkl"
+ model.save_params(path, params)
+
+ global_key, local_key = jax.random.split(rng)
+ local_key = jax.random.fold_in(local_key, process_id)
+
+ # Training state init
+ training_state = _init_training_state(
+ key=global_key,
+ obs_size=obs_size,
+ local_devices_to_use=local_devices_to_use,
+ td3_network=td3_network,
+ policy_optimizer=policy_optimizer,
+ q_optimizer=q_optimizer,
+ )
+ del global_key
+
+ local_key, rb_key, env_key, eval_key = jax.random.split(local_key, 4)
+
+ # Env init
+ env_keys = jax.random.split(env_key, num_envs // jax.process_count())
+ env_keys = jnp.reshape(env_keys, (local_devices_to_use, -1) + env_keys.shape[1:])
+ env_state = jax.pmap(env.reset)(env_keys)
+
+ # Replay buffer init
+ buffer_state = jax.pmap(replay_buffer.init)(
+ jax.random.split(rb_key, local_devices_to_use)
+ )
+
+ if not eval_env:
+ eval_env = environment
+ if randomization_fn is not None:
+ v_randomization_fn = functools.partial(
+ randomization_fn, rng=jax.random.split(eval_key, num_eval_envs)
+ )
+ eval_env = wrap_for_training(
+ eval_env,
+ episode_length=episode_length,
+ action_repeat=action_repeat,
+ randomization_fn=v_randomization_fn,
+ )
+
+ evaluator = acting.Evaluator(
+ eval_env,
+ functools.partial(make_policy, exploration_noise=0, noise_clip=0),
+ num_eval_envs=num_eval_envs,
+ episode_length=episode_length,
+ action_repeat=action_repeat,
+ key=eval_key,
+ )
+
+ # Run initial eval
+ metrics = {}
+ if process_id == 0 and num_evals > 1:
+ metrics = evaluator.run_evaluation(
+ _unpmap((training_state.normalizer_params, training_state.target_policy_params)),
+ training_metrics={},
+ )
+ logging.info(metrics)
+ progress_fn(0, metrics)
+
+ # Create and initialize the replay buffer.
+ t = time.time()
+ prefill_key, local_key = jax.random.split(local_key)
+ prefill_keys = jax.random.split(prefill_key, local_devices_to_use)
+ training_state, env_state, buffer_state, _ = prefill_replay_buffer(
+ training_state, env_state, buffer_state, prefill_keys
+ )
+
+ replay_size = (
+ jnp.sum(jax.vmap(replay_buffer.size)(buffer_state)) * jax.process_count()
+ )
+ logging.info("replay size after prefill %s", replay_size)
+ assert replay_size >= min_replay_size
+ training_walltime = time.time() - t
+
+ current_step = 0
+ for _ in range(num_evals_after_init):
+ logging.info("step %s", current_step)
+
+ # Optimization
+ epoch_key, local_key = jax.random.split(local_key)
+ epoch_keys = jax.random.split(epoch_key, local_devices_to_use)
+ (
+ training_state,
+ env_state,
+ buffer_state,
+ training_metrics,
+ ) = training_epoch_with_timing(
+ training_state, env_state, buffer_state, epoch_keys
+ )
+ current_step = int(_unpmap(training_state.env_steps))
+
+ # Eval and logging
+ if process_id == 0:
+ if checkpoint_logdir:
+ # Save current policy.
+ save_model((training_state.normalizer_params, training_state.target_policy_params), 'actor')
+ save_model((training_state.normalizer_params, training_state.target_q_params), 'critic')
+
+ # Run evals.
+ metrics = evaluator.run_evaluation(
+ _unpmap(
+ (training_state.normalizer_params, training_state.target_policy_params)
+ ),
+ training_metrics,
+ )
+ logging.info(metrics)
+ progress_fn(current_step, metrics)
+
+ total_steps = current_step
+ assert total_steps >= num_timesteps
+
+ params = _unpmap((training_state.normalizer_params, training_state.target_policy_params))
+
+ # If there were no mistakes the training_state should still be identical on all devices.
+ pmap.assert_is_replicated(training_state)
+ logging.info("total steps: %s", total_steps)
+ pmap.synchronize_hosts()
+ return make_policy, params, metrics
diff --git a/brax/training/agents/td3/train_test.py b/brax/training/agents/td3/train_test.py
new file mode 100644
index 000000000..afb695973
--- /dev/null
+++ b/brax/training/agents/td3/train_test.py
@@ -0,0 +1,127 @@
+# Copyright 2024 The Brax Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""TD3 tests."""
+
+import pickle
+
+from absl.testing import absltest
+from absl.testing import parameterized
+from brax import envs
+from brax.training.acme import running_statistics
+import jax
+import networks as td3_networks
+import train as td3
+import jax.numpy as jnp
+
+
+class TD3Test(parameterized.TestCase):
+ """Tests for TD3 module."""
+
+ def testTrain(self):
+ """Test TD3 with a simple env."""
+ fast = envs.get_environment('fast')
+ _, _, metrics = td3.train(
+ fast,
+ num_timesteps=2 ** 15,
+ episode_length=128,
+ num_envs=64,
+ learning_rate=3e-4,
+ discounting=0.99,
+ batch_size=64,
+ normalize_observations=True,
+ reward_scaling=10,
+ grad_updates_per_step=64,
+ num_evals=3,
+ seed=0)
+ self.assertGreater(metrics['eval/episode_reward'], 140 * 0.995)
+ self.assertEqual(fast.reset_count, 2) # type: ignore
+ # once for prefill, once for train, once for eval
+ self.assertEqual(fast.step_count, 3) # type: ignore
+
+ @parameterized.parameters(True, False)
+ def testNetworkEncoding(self, normalize_observations):
+ env = envs.get_environment('fast')
+ original_inference, params, _ = td3.train(
+ env,
+ num_timesteps=128,
+ episode_length=128,
+ num_envs=128,
+ normalize_observations=normalize_observations)
+ normalize_fn = lambda x, y: x
+ if normalize_observations:
+ normalize_fn = running_statistics.normalize
+ td3_network = td3_networks.make_td3_networks(env.observation_size,
+ env.action_size, normalize_fn)
+ inference = td3_networks.make_inference_fn(td3_network)
+ byte_encoding = pickle.dumps(params)
+ decoded_params = pickle.loads(byte_encoding)
+
+ # Compute one action.
+ state = env.reset(jax.random.PRNGKey(0))
+ original_action = original_inference(decoded_params, exploration_noise=0.1, noise_clip=0.1)(state.obs, jax.random.PRNGKey(0))[0]
+ action = inference(decoded_params, exploration_noise=0.1, noise_clip=0.1)(state.obs, jax.random.PRNGKey(0))[0]
+ self.assertSequenceEqual(original_action, action)
+ env.step(state, action)
+
+ def testTrainDomainRandomize(self):
+ """Test with domain randomization."""
+
+ def rand_fn(sys, rng):
+ @jax.vmap
+ def get_offset(rng):
+ offset = jax.random.uniform(rng, shape=(3,), minval=-0.1, maxval=0.1)
+ pos = sys.link.transform.pos.at[0].set(offset)
+ return pos
+
+ sys_v = sys.tree_replace({'link.inertia.transform.pos': get_offset(rng)})
+ in_axes = jax.tree.map(lambda x: None, sys)
+ in_axes = in_axes.tree_replace({'link.inertia.transform.pos': 0})
+ return sys_v, in_axes
+
+ _, _, _ = td3.train(
+ envs.get_environment('inverted_pendulum', backend='spring'),
+ num_timesteps=1280,
+ num_envs=128,
+ episode_length=128,
+ randomization_fn=rand_fn,
+ )
+
+ def test_td3_networks_output_shapes(self):
+ obs_size = 24
+ action_size = 4
+
+ dummy_observation = jnp.ones((obs_size,))
+
+ td3_nets = td3_networks.make_td3_networks(obs_size, action_size)
+
+ key = jax.random.PRNGKey(0)
+ key_policy, key_q = jax.random.split(key)
+ policy_params = td3_nets.policy_network.init(key_policy)
+ q_params = td3_nets.q_network.init(key_q)
+ dummy_preprocess_params = {}
+
+ inference = td3_networks.make_inference_fn(td3_nets)
+ policy_fn = inference((dummy_preprocess_params, policy_params), exploration_noise=0.1, noise_clip=0.1)
+
+ action_output, _ = policy_fn(dummy_observation, jax.random.PRNGKey(0))
+ self.assertEqual(action_output.shape, (action_size,),
+ f"Expected action shape {(action_size,)}, but got {action_output.shape}")
+
+ q_output = td3_nets.q_network.apply(dummy_preprocess_params, q_params, dummy_observation, action_output)
+ self.assertEqual(q_output.shape, (2,), f"Expected Q-value shape {(2,)}, but got {q_output.shape}")
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/notebooks/training.ipynb b/notebooks/training.ipynb
index b71e640d7..3f406a2ba 100644
--- a/notebooks/training.ipynb
+++ b/notebooks/training.ipynb
@@ -1,455 +1,453 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ssCOanHc8JH_"
- },
- "source": [
- "# Training in Brax\n",
- "\n",
- "Once an environment is created in brax, we can quickly train it using brax's built-in training algorithms. Let's try it out!"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ssCOanHc8JH_"
+ },
+ "source": [
+ "# Training in Brax\n",
+ "\n",
+ "Once an environment is created in brax, we can quickly train it using brax's built-in training algorithms. Let's try it out!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "executionInfo": {
+ "elapsed": 22308,
+ "status": "ok",
+ "timestamp": 1679686324614,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ },
+ "user_tz": 240
},
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "executionInfo": {
- "elapsed": 22308,
- "status": "ok",
- "timestamp": 1679686324614,
- "user": {
- "displayName": "",
- "userId": ""
- },
- "user_tz": 240
- },
- "id": "kUrAlZTod7t_"
- },
- "outputs": [],
- "source": [
- "#@markdown ## ⚠️ PLEASE NOTE:\n",
- "#@markdown This colab runs best using a GPU runtime. From the Colab menu, choose Runtime \u003e Change Runtime Type, then select **'GPU'** in the dropdown.\n",
- "\n",
- "import functools\n",
- "import jax\n",
- "import os\n",
- "\n",
- "from datetime import datetime\n",
- "from jax import numpy as jp\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "from IPython.display import HTML, clear_output\n",
- "\n",
- "try:\n",
- " import brax\n",
- "except ImportError:\n",
- " !pip install git+https://github.com/google/brax.git@main\n",
- " clear_output()\n",
- " import brax\n",
- "\n",
- "import flax\n",
- "from brax import envs\n",
- "from brax.io import model\n",
- "from brax.io import json\n",
- "from brax.io import html\n",
- "from brax.training.agents.ppo import train as ppo\n",
- "from brax.training.agents.sac import train as sac\n",
- "\n",
- "if 'COLAB_TPU_ADDR' in os.environ:\n",
- " from jax.tools import colab_tpu\n",
- " colab_tpu.setup_tpu()"
- ]
+ "id": "kUrAlZTod7t_"
+ },
+ "outputs": [],
+ "source": [
+ "#@markdown ## ⚠️ PLEASE NOTE:\n",
+ "#@markdown This colab runs best using a GPU runtime. From the Colab menu, choose Runtime > Change Runtime Type, then select **'GPU'** in the dropdown.\n",
+ "\n",
+ "import functools\n",
+ "import jax\n",
+ "import os\n",
+ "\n",
+ "from datetime import datetime\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from IPython.display import HTML, clear_output\n",
+ "\n",
+ "try:\n",
+ " import brax\n",
+ "except ImportError:\n",
+ " !pip install git+https://github.com/google/brax.git@main\n",
+ " clear_output()\n",
+ " import brax\n",
+ "\n",
+ "from brax import envs\n",
+ "from brax.io import model\n",
+ "from brax.io import html\n",
+ "from brax.training.agents.ppo import train as ppo\n",
+ "from brax.training.agents.sac import train as sac\n",
+ "\n",
+ "if 'COLAB_TPU_ADDR' in os.environ:\n",
+ " from jax.tools import colab_tpu\n",
+ " colab_tpu.setup_tpu()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eoyyw6pQ3xVJ"
+ },
+ "source": [
+ "First let's pick an environment and a backend to train an agent in. \n",
+ "\n",
+ "Recall from the [Brax Basics](https://github.com/google/brax/blob/main/notebooks/basics.ipynb) colab, that the backend specifies which physics engine to use, each with different trade-offs between physical realism and training throughput/speed. The engines generally decrease in physical realism but increase in speed in the following order: `generalized`, `positional`, then `spring`.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "height": 480
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "eoyyw6pQ3xVJ"
- },
- "source": [
- "First let's pick an environment and a backend to train an agent in. \n",
- "\n",
- "Recall from the [Brax Basics](https://github.com/google/brax/blob/main/notebooks/basics.ipynb) colab, that the backend specifies which physics engine to use, each with different trade-offs between physical realism and training throughput/speed. The engines generally decrease in physical realism but increase in speed in the following order: `generalized`, `positional`, then `spring`.\n"
- ]
+ "executionInfo": {
+ "elapsed": 6143,
+ "status": "ok",
+ "timestamp": 1679686333304,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ },
+ "user_tz": 240
},
+ "id": "4hHuDp53e4VJ",
+ "outputId": "90d4c41b-9a25-4ca4-c6be-93172ea9ef57"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "colab": {
- "height": 480
- },
- "executionInfo": {
- "elapsed": 6143,
- "status": "ok",
- "timestamp": 1679686333304,
- "user": {
- "displayName": "",
- "userId": ""
- },
- "user_tz": 240
- },
- "id": "4hHuDp53e4VJ",
- "outputId": "90d4c41b-9a25-4ca4-c6be-93172ea9ef57"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\u003c!DOCTYPE html\u003e\n",
- "\u003chtml\u003e\n",
- "\n",
- " \u003chead\u003e\n",
- " \u003ctitle\u003eBrax visualizer\u003c/title\u003e\n",
- " \u003clink rel=\"shortcut icon\" type=\"image/x-icon\" href=\"/favicon.ico\"\u003e\n",
- " \u003c/head\u003e\n",
- "\n",
- " \u003cstyle\u003e\n",
- " body {\n",
- " margin: 0;\n",
- " padding: 0;\n",
- " }\n",
- "\n",
- " #brax-viewer {\n",
- " height: 480px;\n",
- " margin: 0;\n",
- " padding: 0;\n",
- " }\n",
- " \u003c/style\u003e\n",
- " \u003cscript async src=\"https://unpkg.com/es-module-shims@1.6.3/dist/es-module-shims.js\"\u003e\u003c/script\u003e\n",
- "\n",
- " \u003cscript type=\"importmap\"\u003e\n",
- " {\n",
- " \"imports\": {\n",
- " \"three\": \"https://unpkg.com/three@0.150.1/build/three.module.js\",\n",
- " \"three/addons/\": \"https://unpkg.com/three@0.150.1/examples/jsm/\",\n",
- " \"lilgui\": \"https://cdn.jsdelivr.net/npm/lil-gui@0.18.0/+esm\",\n",
- " \"viewer\": \"https://cdn.jsdelivr.net/gh/google/brax@v0.1.2/brax/v2/visualizer/js/viewer.js\"\n",
- " }\n",
- " }\n",
- " \u003c/script\u003e\n",
- "\n",
- " \u003cscript type=\"application/javascript\"\u003e\n",
- " var system = {\"dt\": 0.005, \"gravity\": [0.0, 0.0, -9.8100004196167], \"link\": {\"transform\": {\"pos\": [[0.0, 0.0, 0.0], [0.20000000298023224, 0.20000000298023224, 0.0], [0.20000000298023224, 0.20000000298023224, 0.0], [-0.20000000298023224, 0.20000000298023224, 0.0], [-0.20000000298023224, 0.20000000298023224, 0.0], [-0.20000000298023224, -0.20000000298023224, 0.0], [-0.20000000298023224, -0.20000000298023224, 0.0], [0.20000000298023224, -0.20000000298023224, 0.0], [0.20000000298023224, -0.20000000298023224, 0.0]], \"rot\": [[1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]], \"name\": \"Transform\"}, \"joint\": {\"pos\": [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], \"rot\": [[1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], 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- " \u003c/script\u003e\n",
- "\n",
- " \u003cbody\u003e\n",
- "\n",
- " \u003cdiv id=\"brax-viewer\"\u003e\u003c/div\u003e\n",
- "\n",
- " \u003cscript type=\"module\"\u003e\n",
- " import {Viewer} from 'viewer';\n",
- " const domElement = document.getElementById(\"brax-viewer\");\n",
- " var viewer = new Viewer(domElement, system);\n",
- " \u003c/script\u003e\n",
- "\n",
- " \u003c/body\u003e\n",
- "\n",
- "\u003c/html\u003e"
- ],
- "text/plain": [
- "\u003cIPython.core.display.HTML object\u003e"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " Brax visualizer\n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ "\n",
+ " \n",
+ "\n",
+ " \n",
+ "\n",
+ " \n",
+ "\n",
+ " \n",
+ "\n",
+ " \n",
+ "\n",
+ ""
],
- "source": [
- "#@title Load Env { run: \"auto\" }\n",
- "\n",
- "env_name = 'ant' # @param ['ant', 'halfcheetah', 'hopper', 'humanoid', 'humanoidstandup', 'inverted_pendulum', 'inverted_double_pendulum', 'pusher', 'reacher', 'walker2d']\n",
- "backend = 'positional' # @param ['generalized', 'positional', 'spring']\n",
- "\n",
- "env = envs.get_environment(env_name=env_name,\n",
- " backend=backend)\n",
- "state = jax.jit(env.reset)(rng=jax.random.PRNGKey(seed=0))\n",
- "\n",
- "HTML(html.render(env.sys, [state.pipeline_state]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "GMailSDb30t-"
- },
- "source": [
- "# Training\n",
- "\n",
- "Brax provides out of the box the following training algorithms:\n",
- "\n",
- "* [Proximal policy optimization](https://github.com/google/brax/blob/main/brax/training/agents/ppo/train.py)\n",
- "* [Soft actor-critic](https://github.com/google/brax/blob/main/brax/training/agents/sac/train.py)\n",
- "* [Evolutionary strategy](https://github.com/google/brax/blob/main/brax/training/agents/es/train.py)\n",
- "* [Analytic policy gradients](https://github.com/google/brax/blob/main/brax/training/agents/apg/train.py)\n",
- "* [Augmented random search](https://github.com/google/brax/blob/main/brax/training/agents/ars/train.py)\n",
- "\n",
- "Trainers take as input an environment function and some hyperparameters, and return an inference function to operate the environment."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "y3MA1UYlftuq"
- },
- "source": [
- "# Training\n",
- "\n",
- "Let's train the Ant policy using the `generalized` backend with PPO."
+ "text/plain": [
+ ""
]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#@title Load Env { run: \"auto\" }\n",
+ "\n",
+ "env_name = 'ant' # @param ['ant', 'halfcheetah', 'hopper', 'humanoid', 'humanoidstandup', 'inverted_pendulum', 'inverted_double_pendulum', 'pusher', 'reacher', 'walker2d']\n",
+ "backend = 'positional' # @param ['generalized', 'positional', 'spring']\n",
+ "\n",
+ "env = envs.get_environment(env_name=env_name,\n",
+ " backend=backend)\n",
+ "state = jax.jit(env.reset)(rng=jax.random.PRNGKey(seed=0))\n",
+ "\n",
+ "HTML(html.render(env.sys, [state.pipeline_state]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GMailSDb30t-"
+ },
+ "source": [
+ "# Training\n",
+ "\n",
+ "Brax provides out of the box the following training algorithms:\n",
+ "\n",
+ "* [Proximal policy optimization](https://github.com/google/brax/blob/main/brax/training/agents/ppo/train.py)\n",
+ "* [Twin delayed deep deterministic policy gradient](https://github.com/google/brax/blob/main/brax/training/agents/td3/train.py)\n",
+ "* [Soft actor-critic](https://github.com/google/brax/blob/main/brax/training/agents/sac/train.py)\n",
+ "* [Evolutionary strategy](https://github.com/google/brax/blob/main/brax/training/agents/es/train.py)\n",
+ "* [Analytic policy gradients](https://github.com/google/brax/blob/main/brax/training/agents/apg/train.py)\n",
+ "* [Augmented random search](https://github.com/google/brax/blob/main/brax/training/agents/ars/train.py)\n",
+ "\n",
+ "Trainers take as input an environment function and some hyperparameters, and return an inference function to operate the environment."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "y3MA1UYlftuq"
+ },
+ "source": [
+ "# Training\n",
+ "\n",
+ "Let's train the Ant policy using the `generalized` backend with PPO."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "height": 321
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "height": 321
- },
- "executionInfo": {
- "elapsed": 190263,
- "status": "ok",
- "timestamp": 1671658344336,
- "user": {
- "displayName": "",
- "userId": ""
- },
- "user_tz": 300
- },
- "id": "FB6G2_Yt4A2m",
- "outputId": "402a0a43-3525-4eca-a425-ffcc71e8db0f"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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AnQARsQrw7TJWr777xBw27tjL7Z882YnErAHKJpnsTSe6CgBJ7XMbktnb/eW1\nVTw+cxXXnT2YEb2Pync4ZlaFbJLJREm/AjpK+jfg7yQjCJvl3LptpXz7sTmM7NuRq88alO9wzOwg\nqu0zSYd8/wMwFNhG0m/y3YiYXA+xWTMXEXzrj69RWlbBzR8bSauW2fztY2b5UG0yiYiQ9FhEjAac\nQKxePTh9Jf9cuJ7/umA4g7odke9wzKwa2fyp96KkU3IeiVmGFRt38oO/zuPMY7vy6bHH5DscM6tB\nNg8tvhf4d0krSO7oEslFy4k5jcyarYrK4GsTZ9GyhfjxxSfSooWfJTFr6LJJJh/MeRRmGX79/FKK\nV2zmZx8fydEdC/MdjpllIZsn4FfURyBmkMyQePPTi/jgiJ5cOKp3vsMxsyzl7PYYSX0l/VPSfElz\nJV2XlneWNFnS6+l7p4w6N0haLGmhpHMzykdLmp1+dqs8hkaTtKe8gq/8YSYdCgv4wYUjPFSKWSOS\ny3sty4GvRcTxwFjgGknDgOuBKRExGJiSrpN+NoFkFsdxwO2S9o0nfgdwFTA4fY3LYdyWJ7f8/XUW\nrNnOTR89wTMkmjUyOUsmEbE6Il5Jl7cD84HewHjg3nSze4EL0+XxwEMRsScdRHIxMEZSL6BDRExL\nn8S/L6OONREzVmzil88u4eNFfTlnWI98h2NmtVQvT4FJ6g+cBLwE9IiI1ZAkHGDfRBS9gZUZ1UrS\nst7p8oHlVX3PVZKKJRWvX7/+sP4Mljs795Tz1YmzOLpjId8+//h8h2NmhyDnySSdofGPwJcjYlt1\nm1ZRFtWUv7Mw4s6IKIqIom7dutU+WMuLH02azxubdvHTS0ZyZNuCfIdjZocgp8lEUgFJIrk/Ih5N\ni9emTVek7+vS8hKgb0b1PsCqtLxPFeXWBDyzcB2/f/ENPnfmAMYO7JLvcMzsEOXybi4BdwHzI+Lm\njI+eAC5Ply8HHs8onyCpjaQBJB3t09OmsO2Sxqb7vCyjjjViW3bt5ZuPvMZxPY7gax8Yku9wzKwO\nsnlo8VCdQTI742xJM9Oy/wBuIhmJ+ErgDeASgIiYK2kiMI/kTrBrIqIirXc1cA/JFMKT0pc1ct95\nfC6bdu7lt585hbYFLWuuYGYNVs6SSUT8i6r7OwDOPkid/wb+u4ryYmDE4YvO8u2JWav486xVfP0D\nx3mOErMmwGN6W71bu62U7zw2h1F9O/L593iOErOmwMnE6lVE8M1HXmNPuecoMWtK/D/Z6tUD09/g\n2UXr+Y/zjmeg5ygxazKcTKzeLN+wkx/8ZT7vGtyVT53qOUrMmhInE6sXFZXB1x6eRauWnqPErCnK\n5a3BZm+587mlzFixmZ9/fBS9jvIcJWZNja9MLOfmrdrGzZMXct4JPRk/6uh8h2NmOeBkYjm1p7yC\nr06cyVGFrfnBhSd4jhKzJsrNXJZTP5uczFFy1+VFdG7fOt/hmFmOOJlYTpRXVHLP1OX86rklTDil\nL2cf7zlKzJoyJxM77F5evonvPDaHBWu2894h3fj2+cPyHZKZ5ZiTiR0267fv4UeT5vPoK2/Su2Mh\nv/r0aD4wrIf7ScyaAScTq7OKyuD+l1bwk78tpLSsgi+cNYgvvu9Y2rX26WXWXPh/u9XJK29s5juP\nzWHuqm2ccWwX/uuCERzb3cOkmDU3TiZ2SDbt3MuPn1rAQy+vpEeHNvzfJ07iQyf0cpOWWTPlZGK1\nUlkZPPTySn78twVsLy3n3941gOvOOY4j2vhUMmvO/BvAsja7ZCvffnwOs1ZuYcyAznx//AiG9Dwy\n32GZWQPgZGI12rqrjJ8+vZDfv7SCLu3b8LOPj+TCUb3dpGVmb8nZcCqSfitpnaQ5GWWdJU2W9Hr6\n3injsxskLZa0UNK5GeWjJc1OP7tV/g1Wbyorg4eLV/K+/32G+19aweWn9ecfX38PHzmpjxOJmb1N\nLsfmugcYd0DZ9cCUiBgMTEnXkTQMmAAMT+vcLqllWucO4CpgcPo6cJ+WA/NWbeNjv5rGNx55jWO6\ntOPPXzqTGy8YToe2BfkOzcwaoJw1c0XEc5L6H1A8HjgrXb4XeAb4Vlr+UETsAZZJWgyMkbQc6BAR\n0wAk3QdcCEzKVdzN3bbSMn42eRH3TVvBUYUF/PjiE7n45D6ef8TMqlXffSY9ImI1QESsltQ9Le8N\nvJixXUlaVpYuH1huOfCv1zfw1YkzWb9jD58Y049vnDuEju08OKOZ1ayhdMBX9WdvVFNe9U6kq0ia\nxOjXr9/hiawZKKuo5ObJi/jls0sY1O0Ifn1ZESP7dsx3WGbWiNR3MlkrqVd6VdILWJeWlwB9M7br\nA6xKy/tUUV6liLgTuBOgqKjooEnH9lu5aRdfevBVZq7cwqVj+vLd84dT2LplzRXNzDLU9+RYTwCX\np8uXA49nlE+Q1EbSAJKO9ulpk9h2SWPTu7guy6hjdfTErFWcd8vzLFm/g1984mR+9NETnUjM7JDk\n7MpE0oMkne1dJZUA3wNuAiZKuhJ4A7gEICLmSpoIzAPKgWsioiLd1dUkd4YVknS8u/O9jnbtLefG\nJ+YysbiEk/t15JYJJ9G3c7t8h2VmjZgimmZrUFFRURQXF+c7jAZn7qqtfOnBV1m2YSfXnHUsXz5n\nMK1aevZmM0tImhERRbWt11A64C3HIoJ7pi7nR08uoFP7Au7/3KmcPqhrvsMysybCyaQZ2LRzL998\nZBZ/n7+Os4d25yeXjPR87GZ2WDmZNHFTl2zgK3+YyeadZdz44WFcfnp/D4ViZoedk0kTVV5RyS1T\nXuf//rmYAV3b89vPnMLwo4/Kd1hm1kQ5mTRBJZt3cd1DM5mxYjMfK+rDjRcM9xS6ZpZT/g3TxDw5\nezXX//E1IuCWCaMYP8qjz5hZ7jmZNBG791bw//4yjwenv8Govh25dcJJ9OviZ0fMrH44mTQBC9Zs\n40sPvMri9Tu4+qxBfPX9x1HgZ0fMrB45mTRSFZXBnDe38o8F6/jls0voUFjA7z57KmcO9rMjZlb/\nnEwaiYjg9XU7mLp4Ay8s2ciLSzeyvbQcgHOO785NF51I1yPa5DlKM2uunEwasJWbdvHC4g1MXbKR\nqUs2smHHHgCO6dKO80/sxWmDunLawC50O9JJxMzyy8mkAVm3rZRpSze+lUBKNu8GoPuRbTjz2C6c\nfmxXTh/UhT6d3LFuZg2Lk0kebd1VxrSlG5m2JEker6/bAcBRhQWcNrALV717IKcP6sqgbu391LqZ\nNWhOJvVo195yXl6+malLNjB18UbmrNpKBBQWtGTMgM5cUtSH0wd15fheHWjpOdfNrBFxMsmhPeUV\nzHxjCy8sSa4+Zq7cQllF0LplC07q15Evn30cpx/bhZF9OtK6lW/lNbPGy8nkMCqvqGTOqm1MXbKB\naUs28vLyTZSWVdJCcELvo/jcuwZy+qAuFB3T2TMamlmT4mRSB5WVwaJ125m6OLnb6qWlG9m+J7ld\nd2jPI7l0TD9OH9SVMQM6c1RhQZ6jNTPLHSeTWogIVmzcxQtph/mLSzaycedeAPp3acf5I4/m9EFd\nGOvbdc2smXEyqca20jJWbNjFwrXbmZb2e6zaWgpAjw5teM9x3ThtUBdO8+26ZtbMNZpkImkccAvQ\nEvhNRNx0OPa7vbSM5Rt2sXzjTpZv2Mmy9H3Fxl1vXXUAdGpXwGmDunD1oORZj4Fdfbuumdk+jSKZ\nSGoJ/AJ4P1ACvCzpiYiYl0397aVlrNi4i2UbdrJi406WpcljxcadbNix923b9uzQlmO6tOP9w3rQ\nv2t7+ndpx8BuR3BstyNo4dt1zcyq1CiSCTAGWBwRSwEkPQSMBw6aTEo27+biO6ayvIqE0aNDG/p3\nac/ZQ5OEMaBrO47p0p5jurTzJFJmZoegsfzm7A2szFgvAU49cCNJVwFXpat7/viFM+ZUtbMVwPTD\nHWHD1hXYkO8gGggfi/18LPbzsdhvyKFUaizJpKr2pXhHQcSdwJ0AkoojoijXgTUGPhb7+Vjs52Ox\nn4/FfpKKD6VeY3nsugTom7HeB1iVp1jMzOwAjSWZvAwMljRAUmtgAvBEnmMyM7NUo2jmiohySV8E\n/kZya/BvI2JuDdXuzH1kjYaPxX4+Fvv5WOznY7HfIR0LRbyj68HMzKxWGkszl5mZNWBOJmZmVmeN\nOplIGidpoaTFkq6v4nNJujX9/DVJJ+cjzvqQxbE4S9JWSTPT13fzEWd9kPRbSeskVfmcUTM7L2o6\nFs3ivJDUV9I/Jc2XNFfSdVVs0yzOiyyPRe3Pi4holC+SjvglwECgNTALGHbANucBk0ieUxkLvJTv\nuPN4LM4C/pLvWOvpeLwbOBmYc5DPm8V5keWxaBbnBdALODldPhJY1Ix/X2RzLGp9XjTmK5O3hliJ\niL3AviFWMo0H7ovEi0BHSb3qO9B6kM2xaDYi4jlgUzWbNJfzIptj0SxExOqIeCVd3g7MJxlZI1Oz\nOC+yPBa11piTSVVDrBx4QLLZpinI9uc8TdIsSZMkDa+f0Bqk5nJeZKtZnReS+gMnAS8d8FGzOy+q\nORZQy/OiUTxnchDZDLGS1TAsTUA2P+crwDERsUPSecBjwOBcB9ZANZfzIhvN6ryQdATwR+DLEbHt\nwI+rqNJkz4sajkWtz4vGfGWSzRArzWUYlhp/zojYFhE70uUngQJJXesvxAaluZwXNWpO54WkApJf\nnvdHxKNVbNJszouajsWhnBeNOZlkM8TKE8Bl6V0aY4GtEbG6vgOtBzUeC0k9lc7mJWkMyb/9xnqP\ntGFoLudFjZrLeZH+jHcB8yPi5oNs1izOi2yOxaGcF422mSsOMsSKpM+nn/8SeJLkDo3FwC7ginzF\nm0tZHouLgasllQO7gQmR3rb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- "text/plain": [
- "\u003cFigure size 600x400 with 1 Axes\u003e"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "time to jit: 0:00:33.268636\n",
- "time to train: 0:02:36.432970\n"
- ]
- }
- ],
- "source": [
- "#@title Training\n",
- "\n",
- "# We determined some reasonable hyperparameters offline and share them here.\n",
- "train_fn = {\n",
- " 'inverted_pendulum': functools.partial(ppo.train, num_timesteps=2_000_000, num_evals=20, reward_scaling=10, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=5, num_minibatches=32, num_updates_per_batch=4, discounting=0.97, learning_rate=3e-4, entropy_cost=1e-2, num_envs=2048, batch_size=1024, seed=1),\n",
- " 'inverted_double_pendulum': functools.partial(ppo.train, num_timesteps=20_000_000, num_evals=20, reward_scaling=10, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=5, num_minibatches=32, num_updates_per_batch=4, discounting=0.97, learning_rate=3e-4, entropy_cost=1e-2, num_envs=2048, batch_size=1024, seed=1),\n",
- " 'ant': functools.partial(ppo.train, num_timesteps=50_000_000, num_evals=10, reward_scaling=10, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=5, num_minibatches=32, num_updates_per_batch=4, discounting=0.97, learning_rate=3e-4, entropy_cost=1e-2, num_envs=4096, batch_size=2048, seed=1),\n",
- " 'humanoid': functools.partial(ppo.train, num_timesteps=50_000_000, num_evals=10, reward_scaling=0.1, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=10, num_minibatches=32, num_updates_per_batch=8, discounting=0.97, learning_rate=3e-4, entropy_cost=1e-3, num_envs=2048, batch_size=1024, seed=1),\n",
- " 'reacher': functools.partial(ppo.train, num_timesteps=50_000_000, num_evals=20, reward_scaling=5, episode_length=1000, normalize_observations=True, action_repeat=4, unroll_length=50, num_minibatches=32, num_updates_per_batch=8, discounting=0.95, learning_rate=3e-4, entropy_cost=1e-3, num_envs=2048, batch_size=256, max_devices_per_host=8, seed=1),\n",
- " 'humanoidstandup': functools.partial(ppo.train, num_timesteps=100_000_000, num_evals=20, reward_scaling=0.1, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=15, num_minibatches=32, num_updates_per_batch=8, discounting=0.97, learning_rate=6e-4, entropy_cost=1e-2, num_envs=2048, batch_size=1024, seed=1),\n",
- " 'hopper': functools.partial(sac.train, num_timesteps=6_553_600, num_evals=20, reward_scaling=30, episode_length=1000, normalize_observations=True, action_repeat=1, discounting=0.997, learning_rate=6e-4, num_envs=128, batch_size=512, grad_updates_per_step=64, max_devices_per_host=1, max_replay_size=1048576, min_replay_size=8192, seed=1),\n",
- " 'walker2d': functools.partial(sac.train, num_timesteps=7_864_320, num_evals=20, reward_scaling=5, episode_length=1000, normalize_observations=True, action_repeat=1, discounting=0.997, learning_rate=6e-4, num_envs=128, batch_size=128, grad_updates_per_step=32, max_devices_per_host=1, max_replay_size=1048576, min_replay_size=8192, seed=1),\n",
- " 'halfcheetah': functools.partial(ppo.train, num_timesteps=50_000_000, num_evals=20, reward_scaling=1, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=20, num_minibatches=32, num_updates_per_batch=8, discounting=0.95, learning_rate=3e-4, entropy_cost=0.001, num_envs=2048, batch_size=512, seed=3),\n",
- " 'pusher': functools.partial(ppo.train, num_timesteps=50_000_000, num_evals=20, reward_scaling=5, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=30, num_minibatches=16, num_updates_per_batch=8, discounting=0.95, learning_rate=3e-4,entropy_cost=1e-2, num_envs=2048, batch_size=512, seed=3),\n",
- "}[env_name]\n",
- "\n",
- "\n",
- "max_y = {'ant': 8000, 'halfcheetah': 8000, 'hopper': 2500, 'humanoid': 13000, 'humanoidstandup': 75_000, 'reacher': 5, 'walker2d': 5000, 'pusher': 0}[env_name]\n",
- "min_y = {'reacher': -100, 'pusher': -150}.get(env_name, 0)\n",
- "\n",
- "xdata, ydata = [], []\n",
- "times = [datetime.now()]\n",
- "\n",
- "def progress(num_steps, metrics):\n",
- " times.append(datetime.now())\n",
- " xdata.append(num_steps)\n",
- " ydata.append(metrics['eval/episode_reward'])\n",
- " clear_output(wait=True)\n",
- " plt.xlim([0, train_fn.keywords['num_timesteps']])\n",
- " plt.ylim([min_y, max_y])\n",
- " plt.xlabel('# environment steps')\n",
- " plt.ylabel('reward per episode')\n",
- " plt.plot(xdata, ydata)\n",
- " plt.show()\n",
- "\n",
- "make_inference_fn, params, _ = train_fn(environment=env, progress_fn=progress)\n",
- "\n",
- "print(f'time to jit: {times[1] - times[0]}')\n",
- "print(f'time to train: {times[-1] - times[1]}')"
- ]
+ "executionInfo": {
+ "elapsed": 190263,
+ "status": "ok",
+ "timestamp": 1671658344336,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ },
+ "user_tz": 300
},
+ "id": "FB6G2_Yt4A2m",
+ "outputId": "402a0a43-3525-4eca-a425-ffcc71e8db0f"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "bjlh7puy2ZM1"
- },
- "source": [
- "The trainers return an inference function, parameters, and the final set of metrics gathered during evaluation.\n",
- "\n",
- "# Saving and Loading Policies\n",
- "\n",
- "Brax can save and load trained policies:"
+ "data": {
+ "image/png": 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AnQARsQrw7TJWr777xBw27tjL7Z882YnErAHKJpnsTSe6CgBJ7XMbktnb/eW1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\nm0tZHouLgasllQO7gQmR3rb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+ "text/plain": [
+ ""
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "gOWeDqlP35sI"
- },
- "outputs": [],
- "source": [
- "model.save_params('/tmp/params', params)\n",
- "params = model.load_params('/tmp/params')\n",
- "inference_fn = make_inference_fn(params)"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "time to jit: 0:00:33.268636\n",
+ "time to train: 0:02:36.432970\n"
+ ]
+ }
+ ],
+ "source": [
+ "#@title Training\n",
+ "\n",
+ "# We determined some reasonable hyperparameters offline and share them here.\n",
+ "train_fn = {\n",
+ " 'inverted_pendulum': functools.partial(ppo.train, num_timesteps=2_000_000, num_evals=20, reward_scaling=10, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=5, num_minibatches=32, num_updates_per_batch=4, discounting=0.97, learning_rate=3e-4, entropy_cost=1e-2, num_envs=2048, batch_size=1024, seed=1),\n",
+ " 'inverted_double_pendulum': functools.partial(ppo.train, num_timesteps=20_000_000, num_evals=20, reward_scaling=10, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=5, num_minibatches=32, num_updates_per_batch=4, discounting=0.97, learning_rate=3e-4, entropy_cost=1e-2, num_envs=2048, batch_size=1024, seed=1),\n",
+ " 'ant': functools.partial(ppo.train, num_timesteps=50_000_000, num_evals=10, reward_scaling=10, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=5, num_minibatches=32, num_updates_per_batch=4, discounting=0.97, learning_rate=3e-4, entropy_cost=1e-2, num_envs=4096, batch_size=2048, seed=1),\n",
+ " 'humanoid': functools.partial(ppo.train, num_timesteps=50_000_000, num_evals=10, reward_scaling=0.1, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=10, num_minibatches=32, num_updates_per_batch=8, discounting=0.97, learning_rate=3e-4, entropy_cost=1e-3, num_envs=2048, batch_size=1024, seed=1),\n",
+ " 'reacher': functools.partial(ppo.train, num_timesteps=50_000_000, num_evals=20, reward_scaling=5, episode_length=1000, normalize_observations=True, action_repeat=4, unroll_length=50, num_minibatches=32, num_updates_per_batch=8, discounting=0.95, learning_rate=3e-4, entropy_cost=1e-3, num_envs=2048, batch_size=256, max_devices_per_host=8, seed=1),\n",
+ " 'humanoidstandup': functools.partial(ppo.train, num_timesteps=100_000_000, num_evals=20, reward_scaling=0.1, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=15, num_minibatches=32, num_updates_per_batch=8, discounting=0.97, learning_rate=6e-4, entropy_cost=1e-2, num_envs=2048, batch_size=1024, seed=1),\n",
+ " 'hopper': functools.partial(sac.train, num_timesteps=6_553_600, num_evals=20, reward_scaling=30, episode_length=1000, normalize_observations=True, action_repeat=1, discounting=0.997, learning_rate=6e-4, num_envs=128, batch_size=512, grad_updates_per_step=64, max_devices_per_host=1, max_replay_size=1048576, min_replay_size=8192, seed=1),\n",
+ " 'walker2d': functools.partial(sac.train, num_timesteps=7_864_320, num_evals=20, reward_scaling=5, episode_length=1000, normalize_observations=True, action_repeat=1, discounting=0.997, learning_rate=6e-4, num_envs=128, batch_size=128, grad_updates_per_step=32, max_devices_per_host=1, max_replay_size=1048576, min_replay_size=8192, seed=1),\n",
+ " 'halfcheetah': functools.partial(ppo.train, num_timesteps=50_000_000, num_evals=20, reward_scaling=1, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=20, num_minibatches=32, num_updates_per_batch=8, discounting=0.95, learning_rate=3e-4, entropy_cost=0.001, num_envs=2048, batch_size=512, seed=3),\n",
+ " 'pusher': functools.partial(ppo.train, num_timesteps=50_000_000, num_evals=20, reward_scaling=5, episode_length=1000, normalize_observations=True, action_repeat=1, unroll_length=30, num_minibatches=16, num_updates_per_batch=8, discounting=0.95, learning_rate=3e-4,entropy_cost=1e-2, num_envs=2048, batch_size=512, seed=3),\n",
+ "}[env_name]\n",
+ "\n",
+ "\n",
+ "max_y = {'ant': 8000, 'halfcheetah': 8000, 'hopper': 2500, 'humanoid': 13000, 'humanoidstandup': 75_000, 'reacher': 5, 'walker2d': 5000, 'pusher': 0}[env_name]\n",
+ "min_y = {'reacher': -100, 'pusher': -150}.get(env_name, 0)\n",
+ "\n",
+ "xdata, ydata = [], []\n",
+ "times = [datetime.now()]\n",
+ "\n",
+ "def progress(num_steps, metrics):\n",
+ " times.append(datetime.now())\n",
+ " xdata.append(num_steps)\n",
+ " ydata.append(metrics['eval/episode_reward'])\n",
+ " clear_output(wait=True)\n",
+ " plt.xlim([0, train_fn.keywords['num_timesteps']])\n",
+ " plt.ylim([min_y, max_y])\n",
+ " plt.xlabel('# environment steps')\n",
+ " plt.ylabel('reward per episode')\n",
+ " plt.plot(xdata, ydata)\n",
+ " plt.show()\n",
+ "\n",
+ "make_inference_fn, params, _ = train_fn(environment=env, progress_fn=progress)\n",
+ "\n",
+ "print(f'time to jit: {times[1] - times[0]}')\n",
+ "print(f'time to train: {times[-1] - times[1]}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bjlh7puy2ZM1"
+ },
+ "source": [
+ "The trainers return an inference function, parameters, and the final set of metrics gathered during evaluation.\n",
+ "\n",
+ "# Saving and Loading Policies\n",
+ "\n",
+ "Brax can save and load trained policies:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "gOWeDqlP35sI"
+ },
+ "outputs": [],
+ "source": [
+ "model.save_params('/tmp/params', params)\n",
+ "params = model.load_params('/tmp/params')\n",
+ "inference_fn = make_inference_fn(params)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4YlZvIG320sK"
+ },
+ "source": [
+ "The trainers return an inference function, parameters, and the final set of metrics gathered during evaluation.\n",
+ "\n",
+ "# Saving and Loading Policies\n",
+ "\n",
+ "Brax can save and load trained policies:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "height": 480
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4YlZvIG320sK"
- },
- "source": [
- "The trainers return an inference function, parameters, and the final set of metrics gathered during evaluation.\n",
- "\n",
- "# Saving and Loading Policies\n",
- "\n",
- "Brax can save and load trained policies:"
- ]
+ "executionInfo": {
+ "elapsed": 33520,
+ "status": "ok",
+ "timestamp": 1679346718844,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ },
+ "user_tz": 240
},
+ "id": "kF5fS-yo35sI",
+ "outputId": "94d1a7d5-8d6e-456c-8cfd-94a689b8808f"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "height": 480
- },
- "executionInfo": {
- "elapsed": 33520,
- "status": "ok",
- "timestamp": 1679346718844,
- "user": {
- "displayName": "",
- "userId": ""
- },
- "user_tz": 240
- },
- "id": "kF5fS-yo35sI",
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- " \u003c/script\u003e\n",
- "\n",
- " \u003cbody\u003e\n",
- "\n",
- " \u003cdiv id=\"brax-viewer\"\u003e\u003c/div\u003e\n",
- "\n",
- " \u003cscript type=\"module\"\u003e\n",
- " import {Viewer} from 'viewer';\n",
- " const domElement = document.getElementById(\"brax-viewer\");\n",
- " var viewer = new Viewer(domElement, system);\n",
- " \u003c/script\u003e\n",
- "\n",
- " \u003c/body\u003e\n",
- "\n",
- "\u003c/html\u003e"
- ],
- "text/plain": [
- "\u003cIPython.core.display.HTML object\u003e"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " Brax visualizer\n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ "\n",
+ " \n",
+ "\n",
+ " \n",
+ "\n",
+ " \n",
+ "\n",
+ " \n",
+ "\n",
+ " \n",
+ "\n",
+ ""
],
- "source": [
- "#@title Visualizing a trajectory of the learned inference function\n",
- "\n",
- "# create an env with auto-reset\n",
- "env = envs.create(env_name=env_name, backend=backend)\n",
- "\n",
- "jit_env_reset = jax.jit(env.reset)\n",
- "jit_env_step = jax.jit(env.step)\n",
- "jit_inference_fn = jax.jit(inference_fn)\n",
- "\n",
- "rollout = []\n",
- "rng = jax.random.PRNGKey(seed=1)\n",
- "state = jit_env_reset(rng=rng)\n",
- "for _ in range(1000):\n",
- " rollout.append(state.pipeline_state)\n",
- " act_rng, rng = jax.random.split(rng)\n",
- " act, _ = jit_inference_fn(state.obs, act_rng)\n",
- " state = jit_env_step(state, act)\n",
- "\n",
- "HTML(html.render(env.sys.tree_replace({'opt.timestep': env.dt}), rollout))"
+ "text/plain": [
+ ""
]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "CBtrAqns35sI"
- },
- "source": [
- "🙌 See you soon!"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "provenance": [],
- "toc_visible": true
- },
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
- },
- "language_info": {
- "name": "python"
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "#@title Visualizing a trajectory of the learned inference function\n",
+ "\n",
+ "# create an env with auto-reset\n",
+ "env = envs.create(env_name=env_name, backend=backend)\n",
+ "\n",
+ "jit_env_reset = jax.jit(env.reset)\n",
+ "jit_env_step = jax.jit(env.step)\n",
+ "jit_inference_fn = jax.jit(inference_fn)\n",
+ "\n",
+ "rollout = []\n",
+ "rng = jax.random.PRNGKey(seed=1)\n",
+ "state = jit_env_reset(rng=rng)\n",
+ "for _ in range(1000):\n",
+ " rollout.append(state.pipeline_state)\n",
+ " act_rng, rng = jax.random.split(rng)\n",
+ " act, _ = jit_inference_fn(state.obs, act_rng)\n",
+ " state = jit_env_step(state, act)\n",
+ "\n",
+ "HTML(html.render(env.sys.tree_replace({'opt.timestep': env.dt}), rollout))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CBtrAqns35sI"
+ },
+ "source": [
+ "🙌 See you soon!"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
},
- "nbformat": 4,
- "nbformat_minor": 0
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
}