Policy Gradient is a popular method in the field of Reinforcement Learning (RL) that allows agents to learn optimal policies by directly optimizing the expected return. It's a part of the family of actor-critic methods, where the "actor" learns the policy and the "critic" evaluates the policy.

Key Concepts

  • Policy: A policy is a mapping from states to actions. In other words, it tells the agent what action to take in a given state.
  • Gradient: In the context of optimization, a gradient is a vector that points in the direction of the steepest increase of a function.
  • Return: The return, also known as the cumulative reward, is the sum of rewards received over time.

How It Works

  1. Initialize the Policy: Start with an initial policy.
  2. Collect Data: Use the policy to interact with the environment and collect data.
  3. Estimate the Gradient: Compute the gradient of the expected return with respect to the policy parameters.
  4. Update the Policy: Use the estimated gradient to update the policy parameters.
  5. Repeat: Go back to step 2 until the policy is optimal.

Types of Policy Gradient Methods

  • REINFORCE: A Monte Carlo method that directly samples episodes from the current policy.
  • PPO (Proximal Policy Optimization): An on-policy actor-critic method that uses a clipped surrogate objective to improve stability.
  • A2C (Asynchronous Advantage Actor-Critic): An off-policy actor-critic method that uses multiple actors to improve sample efficiency.

Challenges

  • Exploration-Exploitation Dilemma: Finding the right balance between exploring new actions and exploiting known good actions.
  • Credit Assignment: Determining which actions contributed to the current return.

Example

Imagine an agent learning to play a video game. The policy could be a function that maps the current state of the game (e.g., position, velocity) to a set of actions (e.g., jump, move left). The agent would then collect data by playing the game, estimate the gradient of the expected return with respect to the policy parameters, and update the policy accordingly.

Deep Learning

For more information on Policy Gradient, check out our Reinforcement Learning Guide.