What are Policy-Gradient Methods?
Policy-gradient methods are a class of reinforcement learning algorithms that optimize the policy directly, without the need for a value function. Unlike value-based methods like Deep Q-Learning, which first estimate action values and then derive a policy, policy-gradient techniques adjust the policy's parameters to maximize the expected cumulative reward.
Overview of Policy Gradients
In policy-gradient methods, the policy is typically stochastic — it outputs a probability distribution over actions given a state. The goal is to increase the probability of actions that lead to higher returns. After collecting episodes, we compute the return and use it to update the policy via gradient ascent, reinforcing good actions.
Advantages
- Simplicity: Direct optimization of the policy without storing additional value estimates.
- Stochastic Policies: Naturally handle exploration and perceptual aliasing (when different states appear similar).
- No Explicit Exploration: The stochasticity of the policy inherently explores the action space.
Disadvantages
- High Variance: Monte Carlo methods like REINFORCE can have high variance, requiring more samples.
- Sample Inefficiency: Often need many episodes to converge compared to value-based methods.
REINFORCE (Monte Carlo Policy Gradient)
REINFORCE is a classic policy-gradient algorithm that uses the full return from an episode to update the policy. The update rule increases the log-probability of actions weighted by the cumulative reward.
Ready to Code?
Now that you understand the theory, it's time to implement a REINFORCE agent in PyTorch. Test it on classic environments like CartPole-v1, PixelCopter, and Pong. Start the interactive tutorial on Google Colab:
Compare your results on the leaderboard:
Keep learning, stay awesome!