Agent-Environment Interaction
Reinforcement learning involves an agent interacting with an environment and learning to take actions that maximize cumulative rewards over time.
Reward Signal
The agent receives a reward signal from the environment as feedback for its actions. The goal of the agent is to learn a policy that maximizes the expected cumulative reward.
Exploration vs. Exploitation
Reinforcement learning algorithms balance exploration (trying out new actions to learn their effects) and exploitation (taking actions that are known to yield high rewards based on experience).
Algorithms
Reinforcement learning algorithms include:
- Q-learning
- Deep Q-Networks (DQN)
- Policy gradient methods
- Actor-Critic methods
Applications
Reinforcement learning has applications in:
- Game playing (e.g., AlphaGo)
- Robotics
- Autonomous vehicles
- Recommender systems
- Finance