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Mastering Robotic Manipulation with Reinforcement Learning: TQC and DDPG for Fetch Environments

Using reinforcement learning (RL), specifically Truncated Quantile Critics (TQC) and Deep Deterministic Policy Gradient (DDPG), to solve the Fetch environments in Gymnasium Robotics

Beginner's Guide to Model-Based Reinforcement Learning (MBRL) with Atari's Breakout

Learn Model-Based Reinforcement Learning: Build sample-efficient RL agents by modeling environment dynamics and planning with Python in tasks like Atari's Breakout

Comparing how PPO, SAC, and DQN Perform on Gymnasium's Lunar Lander

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

Serving Up Some Robotics: Setting Up a Tennis Environment in MuJoCo

Build a MuJoCo robot tennis simulation! Learn to set up a wall tennis environment, tackle physics/control challenges, understand its architecture, and improve it for robotics or reinforcement learning projects with MuJoCo

Solving Gymnasium's Lunar Lander with Deep Q Learning (DQN)

Learn how the Reinforcement Learning Algorithm Deep Q Learning (DQN) works and apply it to solve Gymnasium's Lunar Lander

Michael Kudlaty

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July 1, 2024