Overview
Optimal value functions are crucial in reinforcement learning as they guide agents in making decisions that maximize expected returns. By understanding how to calculate and implement these functions, learners can develop more effective reinforcement learning models. The Bellman equation serves as a ...
Key Terms
Example: V(s) represents the value of state s.
Example: π*(s) is the optimal action for state s.
Example: V(s) = R(s) + γ * Σ P(s'|s,a)V(s').
Example: A discount factor of 0.9 means future rewards are valued at 90%.
Example: MDPs are used to define the environment in RL.
Example: An agent must balance trying new strategies and using successful ones.
Related Topics
Reinforcement Learning Algorithms
Explore various algorithms used in reinforcement learning, including Q-learning and SARSA.
intermediateDeep Reinforcement Learning
Learn how deep learning techniques are applied to reinforcement learning problems.
advancedMulti-Agent Reinforcement Learning
Study how multiple agents can learn and interact in shared environments.
advancedKey Concepts
Value FunctionOptimal PolicyBellman EquationDiscount Factor