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5 posts
Using reinforcement learning (RL), specifically Truncated Quantile Critics (TQC) and Deep Deterministic Policy Gradient (DDPG), to solve the Fetch environments in Gymnasium Robotics
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Train an RL agent to master Atari's Pong with sparse rewards and high-dimensional inputs. Explore preprocessing, replay buffers, and performance-boosting strategies
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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.
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Explore how different On-Policy and Off-Policy reinforcement learning algorithms perform on Gymnasium's Lunar Lander
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Learn how the Reinforcement Learning Algorithm Deep Q Learning (DQN) works and apply it to solve Gymnasium's Lunar Lander
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