Learn Model-Based Reinforcement Learning: Build sample-efficient RL agents by modeling environment dynamics and planning with Python in tasks like Atari's Breakout
Discover how self-play can be used to train a reinforcement learning agent to master Connect 4, achieving advanced strategies without human intervention
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
