Policy Optimization is a key concept in reinforcement learning, focusing on learning an optimal policy to maximize the expected cumulative reward. This tutorial will guide you through the basics of Policy Optimization.
What is Policy Optimization?
Policy Optimization is a family of algorithms that learn policies directly by optimizing a policy parameterization. Instead of learning a value function or a model, it focuses on finding a policy that maps states to actions.
Types of Policy Optimization Algorithms
- Policy Gradient Methods: Update the policy parameters directly based on the gradient of the expected return.
- Actor-Critic Methods: Combine the strengths of policy gradient methods and value-based methods to learn both the policy and a value function.
Key Concepts
- State: The current situation or context in which the agent operates.
- Action: The decision made by the agent in response to the state.
- Reward: The feedback received by the agent after taking an action.
Getting Started
To dive deeper into Policy Optimization, you might want to check out our comprehensive guide on Reinforcement Learning.
Example
Let's say you are training an agent to play a game. The state is the current board configuration, the actions are the moves the agent can make, and the reward is the score gained from making the move.