This is a framework for the research on multi-agent reinforcement learning and the implementation of the experiments in the paper titled by ''Shapley Q-value: A Local Reward Approach to Solve Global Reward Games''.

A deep reinforcement learning system for optimizing bridge maintenance decisions across municipal infrastructure fleets, implementing cross-subsidy budget sharing and cooperative multi-agent learning.

Adversarial Co-Evolution of RL and LLM Agents: A framework for training high-performance PPO agents against Large Language Models in Gin Rummy, utilizing curriculum learning and knowledge distillation.

Curator-guided curriculum learning for zero-shot coordination in Overcooked-AI using multi-agent reinforcement learning.

Dark Zero Point Genesis: PPO Latent World Models Under Thermodynamic Scarcity 256 agents. 128D Latent Manifolds. Zero supervision. Agents utilize PPO-clipped surrogate objectives. Survival = Predictive Error Coding (PEC) × Energy Efficiency across a 50/15 Seasonal Cycle.

Going through the Hugging Face Deep Reinforcement Learning course.

Deterministic hex-grid soccer environment with two adversarial agents. Implements Q-Learning, Minimax-Q (via LP), and Belief-Q with online belief updates; trains in SE2G/SE6G to reduce state space and evaluates behaviors in the full environment with comprehensive visualizations.

크레이지아케이드 모작 + Reinforcement Learning ( DQN, PPO )

🎯 Key Features 1. Flexible Game Configuration Adjustable grid size (3×3 up to 10×10) Customizable win condition (e.g., 5-in-a-row on a 7×7 board) 2. Two Competing RL Agents Agent 1 (Blue X) vs Agent 2 (Red O) Each has independent Q-Learning parameters Watch them evolve different strategies over time

Hexapawn Game Engine Proper 3×3 board with pawn movement Strategic RL Agents Minimax with Alpha-Beta Pruning (depth configurable 1-7) Q-Learning with temporal difference updates Experience replay for efficient learning Epsilon-greedy exploration with decay Multi-level decision hierarchy (immediate threats → strategic planning)

An engineering-focused multi-agent reinforcement learning system for Texas Hold’em using PettingZoo AEC and a custom PyTorch PPO self-play setup.

Project 3 of Udacity's Deep Reinforcement Learning Nanodegree Program

Research-grade Reinforcement Learning framework for single-agent and multi-agent warehouse navigation using Deep Q-Networks (DQN), PyTorch, replay buffer, target networks, logging, and full test suite. Built for PhD-level RL and autonomous systems research.

Multi-Agent Reinforcement Learning based Edge-Cloud Computing Offloading Framework using MAPPO and PureEdgeSim for 6G Networks.

20 RL basics notebooks + 10 advanced projects with Streamlit apps covering RLHF, Offline RL, Multi-Agent RL, Safe RL, World Models, RLAIF, Hierarchical RL, Meta-RL, Drug Discovery RL, and Sim-to-Real transfer

Multi-Equipment CBM system using QR-DQN with advanced probability distribution analysis. Coordinated maintenance decision-making for 4 industrial equipment units with realistic anomaly rates (1.9-2.2%), comprehensive risk analysis (VaR/CVaR), and 51-quantile distribution visualization.

Classic Nim Rules - 3 customizable piles, take any number from one pile per turn, last to take loses Q-Learning Agents - Two independent agents that learn optimal strategy through self-play

Pure RL Agents: I implemented Q-Learning agents that learn through Self-Play. They play against each other to get smarter without human help! Symmetry Optimization: To make them "genius" faster, I added logic so they understand that a board mirrored left-to-right is the same situation. This cuts the learning time in half!

Coordinated multi-agent systems that learn to solve complex collaborative and competitive tasks.

Comprehensive Qwirkle RL agent application. We'll use Monte Carlo Tree Search (MCTS) combined with Q-Learning - the best approaches for tile-placement games with high branching factors.