Explore a technical case study on 'Reinforce Tactics,' a new RL environment where traditional game theory outperforms the latest LLMs by a massive margin, exposing critical flaws in token-based reasoning.

A deep technical walkthrough of building a biomechanically faithful T-Rex in MuJoCo and training it to balance, walk, and hunt through three-stage curriculum learning with PPO — achieving a 96.7% bite success rate on commodity GPU hardware. Covers MJCF model design, Gymnasium environment architecture, reward engineering with mathematical formulations, and real training results from the Mesozoic Labs open-source project.

Train an RL agent to master Atari's Pong with sparse rewards and high-dimensional inputs. Explore preprocessing, replay buffers, and performance-boosting strategies

Learn how to apply reinforcement learning to solve Gymnasium's Car Racing game, see how different algorithms perform, and explore whether discrete or continuous action spaces are better.

Explore how different On-Policy and Off-Policy reinforcement learning algorithms perform on Gymnasium's Lunar Lander