A gridworld warehouse robot solved with tabular Q-learning, then DQN and Rainbow
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Updated
Sep 7, 2026 - Jupyter Notebook
A gridworld warehouse robot solved with tabular Q-learning, then DQN and Rainbow
Balloon Flight Custom Ray environment
reinforcement-learning examples
practice for my thesis
Deep Reinforcement Project in Malmo - UCI CS175 Fall 2020
An attempt to train a NN for the game Briscola Chiamata using rllib (still very preliminary)
muzero Algorithm Reinforcement Learning for Chinese XiangQi
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads
Multi-agent RL environment for fairness-aware delivery fleet dispatch — PettingZoo + RLlib, calibrated on real NYC TLC data. Includes human-annotation, automated QA gating, checkpoint fine-tuning, synthetic scenario generation, distributed rollout benchmarking, and a versioned benchmark suite.
A high-fidelity grid-world RL simulation environment for training human agents in extreme extraterrestrial conditions
A minimalist multi-agent implementation of the social dilemma problem with governance kernels
État de l'art du Multi-Agent RL avec RLlib (Ray 2.56, new API stack) : 13 pages en français, 6 exemples exécutés, 3 expériences mesurées.
Solving gym environments using RLlib: Industry-Grade Reinforcement Learning
Source code of experiments with MARL and FaaS of Emanuele Petriglia.
Pokemon Reinforcement Learning with RLlib
PPO : le papier de Schulman et al. (2017), et ce que Ray RLlib en a fait. Écarts papier/code sur PPO, GAE et le multi-agent.
Fork of ray-project/ray. Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Reinforcement learning algorithm that blends the N-th order Markov property with abstract MDPs, PPO, and a hybrid model-free/model-based approach.
A collection of Jupiter notebook containing reinforcement-learning-based solutions to various classic problems
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