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''.

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.

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

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.

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-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.

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

Multi-Equipment CBM (Condition-Based Maintenance) optimization using Deep Q-Learning with cost leveling and scenario comparison. Advanced RL system with QR-DQN, N-step learning, and parallel environments for HVAC equipment predictive maintenance.