Policy Gradient with PyTorch – Hugging Face Deep RL Class Unit 5

TL;DR

Hugging Face published a hands‑on tutorial that implements the REINFORCE (Monte‑Carlo policy‑gradient) algorithm in PyTorch and benchmarks it on CartPole‑v1, PixelCopter, and Pong, marking the completion of Unit 5 of their free Deep Reinforcement Learning class.

What are Policy‑Gradient Methods?

Policy‑gradient methods belong to the broader class of policy‑based reinforcement‑learning algorithms that optimize the policy directly without learning an intermediate value function. They adjust the parameters (\theta) of a stochastic policy (\pi_{\theta}(a\mid s)) by performing gradient ascent on an objective that measures expected return.

Overview of Policy Gradients

The goal of reinforcement learning is to find a policy that maximizes the expected cumulative reward. In policy‑gradient approaches, the policy outputs a probability distribution over actions for each state. By sampling episodes, computing the total return (R(\tau)), and adjusting (\theta) to increase the log‑probability of actions that led to high returns, the algorithm steers the policy toward more rewarding behavior.

Advantages of Policy‑Gradient Methods

  1. Simplicity – No need to store or estimate action‑value tables; the algorithm updates the policy directly.
  2. Stochastic policies – The agent naturally explores by sampling from its action distribution, eliminating the need for hand‑crafted exploration strategies.
  3. Robustness to perceptual aliasing – In ambiguous states, a stochastic policy can randomize actions, avoiding the dead‑ends that deterministic policies may encounter.
  4. Scalability to high‑dimensional or continuous action spaces – Unlike Deep Q‑Learning, which must evaluate a Q‑value for each discrete action, policy gradients output a distribution that can represent infinitely many actions.

Disadvantages of Policy‑Gradient Methods

  • Local optima – Gradient ascent can converge to sub‑optimal policies.
  • Sample inefficiency – Training may require many episodes because updates are based on Monte‑Carlo returns.
  • High variance – Gradient estimates can be noisy; variance‑reduction techniques (e.g., baselines) are often needed.

For a deeper dive into the pros and cons, the article links to a YouTube explanation.

REINFORCE (Monte‑Carlo Policy Gradient)

REINFORCE updates the policy parameters using the return from an entire episode:

  1. Collect an episode (\tau) by executing the current policy (\pi_{\theta}).
  2. Estimate the gradient (\hat{g}=\nabla_{\theta} J(\theta)) where [ J(\theta)=\mathbb{E}{\tau\sim\pi{\theta}}[R(\tau)] ] and the gradient is approximated by (\nabla_{\theta}\log \pi_{\theta}(a_t\mid s_t),R(\tau)) for each time step.
  3. Update the policy with learning rate (\alpha): [ \theta \leftarrow \theta + \alpha \hat{g} ]

The term (\nabla_{\theta}\log \pi_{\theta}(a_t\mid s_t)) points in the direction of steepest increase of the log‑probability of the taken action, while the return (R(\tau)) scales this direction: high returns push up the probabilities of the observed state‑action pairs, low returns push them down.

Hands‑On Implementation with PyTorch

The tutorial provides a Colab notebook that:

  • Defines a stochastic policy network in PyTorch.
  • Samples full episodes from three environments – CartPole‑v1, PixelCopter, and Pong.
  • Computes episode returns and applies the REINFORCE update rule.
  • Logs performance to a public leaderboard where learners can compare scores.

Resources

Educational Context and Next Steps

This unit concludes the policy‑gradient segment of the Deep Reinforcement Learning Class – a free, beginner‑to‑expert curriculum hosted by Hugging Face. After completing the REINFORCE implementation, learners are encouraged to:

  • Experiment with additional environments to solidify understanding.
  • Review supplemental reading material linked in the syllabus.
  • Prepare for the next unit, which introduces Actor‑Critic methods that combine policy‑based and value‑based learning.

Feedback is collected via a Google Form to iteratively improve the course.

Takeaway

Hugging Face’s new tutorial equips learners with a complete, from‑scratch PyTorch implementation of the REINFORCE algorithm, demonstrates its applicability across classic control and Atari‑style tasks, and serves as a gateway to more advanced hybrid methods such as Actor‑Critic.

Sources