Policy Gradient Methods are a class of reinforcement learning algorithms that estimate the policy directly by gradient descent. They are particularly useful when the state space is large or continuous.

Overview

  • Policy Gradient: The core idea is to learn a policy that maps states to actions directly.
  • Gradient Descent: We use gradient descent to optimize the policy parameters.
  • Advantage Function: This function helps us to evaluate the quality of the policy.

Key Concepts

  • Policy: A function that maps states to probabilities of taking actions.
  • Value Function: A function that estimates the expected return from a state.
  • Reward: The feedback signal received from the environment.

Steps

  1. Initialize: Start with a random policy.
  2. Sample Actions: Use the policy to sample actions from the environment.
  3. Observe Rewards: Receive rewards from the environment.
  4. Update Policy: Use the gradient descent to update the policy parameters based on the rewards.

Example

Here is a simple example of a Policy Gradient algorithm:

# Example Policy Gradient Algorithm

For more details on Policy Gradient Methods, you can check out our Reinforcement Learning Tutorial.

Visualizing Policy Gradient

To understand how Policy Gradient Methods work, it's helpful to visualize the updates in the policy. Here is an image that illustrates this concept:

Policy Gradient Update

This image shows how the policy parameters are updated over time to maximize the expected reward.

Conclusion

Policy Gradient Methods are a powerful tool for reinforcement learning. They allow us to learn policies directly from the environment without needing to estimate the value function.