INFO
Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards. The goal is to learn a policy that maximizes cumulative reward over time.
Overview
Unlike supervised learning, RL does not rely on labeled input-output pairs. Instead, it learns from experience, using trial and error to discover optimal behaviors. RL is particularly suited for sequential decision-making problems where actions influence future states and rewards.
RL problems are typically formalized as Markov Decision Processes (MDPs), defined by:
- States (S): Represent the environment at a given time
- Actions (A): Choices available to the agent
- Transition Function (T): Probability of moving from one state to another
- Reward Function (R): Feedback signal for each action taken
- Policy (π): Strategy that maps states to actions
Key Concepts
- Exploration vs. Exploitation: Balancing the search for new strategies with leveraging known ones
- Value Function: Estimates expected future rewards from a state or state-action pair
- Policy Optimization: Learning the best strategy for decision-making
- Temporal Difference Learning: Updates value estimates based on differences between successive predictions
- Model-Free vs. Model-Based RL: Whether the agent learns without or with an explicit model of the environment
- On-Policy vs. Off-Policy: Whether the agent learns from its own actions or from a separate behavior policy
Related Topics
- Monte Carlo Tree Search — Monte Carlo Tree Search: A planning algorithm often used in hybrid RL systems for decision-making under uncertainty
- Policy Gradient Methods — Probabilistic Graphical Models: Useful for modeling structured uncertainty and reasoning in model-based or hybrid RL approaches
Applications
- Autonomous control and navigation
- Game-playing agents
- Robotics and manipulation
- Industrial process optimization
- Financial decision-making
- Multi-agent coordination
Suggested Links
- Reinforced Deep Learning Models — Deep RL algorithms using neural networks
- Hybrid Deep Learning Models — For architectures combining RL with other paradigms
- Model Evaluation — For metrics and validation strategies in RL
- Supervised Learning — For comparison with supervised paradigms