policy-gradient-methods

What problem does it solve?

This Skill helps practitioners apply policy-gradient methods to optimize decision policies in continuous-action tasks, reducing the barrier to implementing reinforcement learning from scratch.

Core Features & Use Cases

• Comprehensive guidance on REINFORCE, PPO, and TRPO, including their strengths, weaknesses, and practical tradeoffs.
• Algorithm selection framework for common control tasks, with decision criteria based on action space, sample efficiency, and stability.
• Implementation tips covering baselines, advantage estimation (GAE), clipping vs KL constraints, entropy bonuses, and debugging heuristics.
• Real-world scenarios such as robotic control and simulation-based optimization to illustrate how to choose and tune policy gradient methods.

Quick Start

Run a minimal PPO experiment on CartPole-v1 to observe policy improvement across episodes and compare performance with a baseline REINFORCE implementation.