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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.
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Active DevelopmentReinforce TacticsStrategy game made for RLA 2D strategy game built for exploring reinforcement learning strategies. Testing various RL algorithms in competitive multi-agent scenarios with complex state spaces.
Active DevelopmentMesozoic LabsBuilding robotic dinosaursBuilding robotic dinosaurs to explore biomechanics, locomotion, and control systems. Combining paleontology with modern robotics and machine learning for movement optimization.
Coming SoonCourtside DynamicsTeaching agents to rallyA progression of MuJoCo environments and deep reinforcement-learning tools working toward humanoid tennis. Moves from simple ball control through racket-and-wall tasks to an experimental two-humanoid curriculum.
Coming SoonDownforce RoboticsMaking decisions at speedBuilding a self-driving race car from the ground up. Implementing perception, planning, and control stacks with a focus on high-speed autonomous navigation and racing line optimization.
Explores how the ViZDoom platform drives advancements in embodied AI, detailing the evolution from standard Deep Q-Networks to hierarchical architectures capable of mastering complex, 3D environments
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Explore DreamerV3, the AI that taught itself to find diamonds in Minecraft, revolutionizing reinforcement learning with its powerful world model.
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Exploring training a drone with reinforcement learning, focusing on the trade-offs between sparse and dense rewards to achieve agile flight while avoiding unintended "reward hacking."
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Discover the ultimate guide to contextual bandits, covering everything from core theory and key algorithms to a complete Python implementation with code for building powerful personalization and recommendation systems
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A complete guide to the AutoDRIVE ecosystem. Learn to train self-driving car agents from scratch using reinforcement learning in this powerful Unity-based simulator.
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Explore Agentic AI, the next frontier in machine learning. Discover how autonomous agents learn, act independently, and are revolutionizing the field of robotics.
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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
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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
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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.
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A walkthrough of reinforcement learning for stock trading with FinRL: the MDP and Bellman theory underneath it, building a trading environment, and training and backtesting agents with Stable-Baselines3.
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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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A beginner's walkthrough of Model-Based Reinforcement Learning on Atari's Breakout: training an action-conditional world model that predicts frames, rewards and terminals, then learning a PPO policy entirely inside it with SimPLe
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Discover how self-play can be used to train a reinforcement learning agent to master Connect 4, achieving advanced strategies without human intervention
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学习强化学习算法深度 Q 学习（DQN）的工作原理，并将其应用于解决 Gymnasium 的 Lunar Lander 问题。
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