Introduction to Policy Gradients

Policy Gradients (PG) are a class of Reinforcement Learning (RL) algorithms that directly optimize the policy by learning a gradient of the expected reward with respect to the policy parameters. Unlike value-based methods, PG focuses on maximizing the reward through stochastic policies.

Key concepts:

  • Stochastic Policy: A policy that outputs action probabilities
  • Gradient Ascent: Optimization technique to maximize reward
  • Policy Network: Neural network that represents the policy

🚀 Applications:

  • Robotics control
  • Game playing (e.g., AlphaGo)
  • Autonomous systems

How Policy Gradients Work

  1. Policy Representation: Use a neural network to model the policy
  2. Reward Estimation: Calculate the expected reward for each action
  3. Gradient Calculation: Compute the gradient of the reward with respect to the policy parameters
  4. Parameter Update: Adjust the policy using gradient ascent

🔍 Advantages:

  • Directly learns optimal policies
  • Handles high-dimensional action spaces
  • No need for value function approximation

⚠️ Challenges:

  • High variance in gradient estimates
  • Requires careful exploration strategies
  • Computationally expensive

Practical Implementation

For hands-on practice, explore our Actor-Critic Methods Tutorial to understand how to combine policy gradients with value functions.

Further Reading

Reinforcement Learning Overview provides a comprehensive introduction to RL concepts.

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