This Simulation Implements the Concept of Reinforcement learning, where a Car "learns" to avoid obstacles by Trial and Error, and gets better with Time at achieving its End goal.
- In Supervised Learning, there is an "answer" for "data". You can decide upon some method and build a model to be able to predict upon unknown values.
- In Reinforcement learning, There is no way to decide upon a fixed model.
- All you know is - You have to reach a goal.
- How ? Trial and Error.
- Consistst of an agent, and no other useful data to help it achieve its goal.
- It learns from "experience" - collecting training examples through Trial and error as it attempts to reach the goal.
- For every right step, the agent is rewarded
- For every mistake, agent is punished.
Best example : Self Driving Car
- Basic Javascript,HTML,CSS