Reinforcement Learning (RL) is a paradigm where an agent learns to make a sequence of decisions by interacting with an environment to maximize a cumulative reward.

The RL Framework

  • Agent: The decision-maker.
  • Environment: The world the agent interacts with.
  • State (S): The current situation of the agent.
  • Action (A): What the agent can do.
  • Reward (R): The feedback from the environment.

Modern RL Algorithms

Deep RL combines RL with deep neural networks. Key algorithms include PPO (Proximal Policy Optimization), which is known for its stability and ease of use, and DQN (Deep Q-Network), which revolutionized RL in game environments.

Exploration vs Exploitation

  • Exploration: Trying new actions to discover their rewards.
  • Exploitation: Using known information to choose the action with the highest reward.
  • Balancing these two is the core challenge of RL.