Finding Theta

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Mujoco Tennis Part 1

Bridging Worlds: How Visual Language Action Models are Teaching Robots to See, Understand, and Act

Understand how Visual Language Action Models (VLAs) let robots follow commands by fusing AI vision and language for action, demonstrated with a conceptual Python robotics simulation

From Zero to Dino-Roar: Teaching a T-Rex to Walk with MuJoCo and Reinforcement Learning

Build a bipedal T-Rex model in MuJoCo and lay the groundwork for teaching it to walk using reinforcement learning techniques like PPO or SAC. Get the XML, Python setup, and conceptual RL overview

Using Reinforcement Learning for Stock Trading with FinRL

Learn stock trading with Reinforcement Learning (RL) and FinRL. This guide covers RL theory, FinRL setup, agent training (DQN, PPO), backtesting, and practical considerations, including code examples and pitfalls.

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

Using Multi-Agent Reinforcement Learning to play OpenSpiel's Connect 4 with Ray's RLlib

Discover how self-play can be used to train a reinforcement learning agent to master Connect 4, achieving advanced strategies without human intervention

Mastering Atari's Pong with Reinforcement Learning: Overcoming Sparse Rewards and Optimizing Performance

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

Solving Gymnasium's Car Racing with Reinforcement Learning

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.

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

Michael Kudlaty

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August 1, 2024

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

Lastest articles

Mujoco Tennis Part 1

Bridging Worlds: How Visual Language Action Models are Teaching Robots to See, Understand, and Act

From Zero to Dino-Roar: Teaching a T-Rex to Walk with MuJoCo and Reinforcement Learning

Using Reinforcement Learning for Stock Trading with FinRL

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

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

Using Multi-Agent Reinforcement Learning to play OpenSpiel's Connect 4 with Ray's RLlib

Mastering Atari's Pong with Reinforcement Learning: Overcoming Sparse Rewards and Optimizing Performance

Solving Gymnasium's Car Racing with Reinforcement Learning

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