Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, and the goal is to learn a policy that maximizes cumulative rewards over time.
Reinforcement learning is commonly used in scenarios where the agent's actions affect the environment, such as in robotics, game playing, and autonomous control systems.
In PyBrain, reinforcement learning is implemented using various algorithms that enable agents to learn optimal policies through trial and error.
Here's an overview of reinforcement learning and how it can be implemented in PyBrain :
* Environment
* Agent
* Reward Function
* Learning Algorithm
* Q-learning
* SARSA
* Policy Gradients
* Training Loop
