A minimalist environment for decision-making in autonomous driving
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Updated
May 29, 2026 - Python
A minimalist environment for decision-making in autonomous driving
Simple and easily configurable grid world environments for reinforcement learning
Reinforcement Learning environments based on the 1993 game Doom ![]()
Multi-objective Gymnasium environments for reinforcement learning
An API conversion tool for popular external reinforcement learning environments
A framework for creating rich, 3D, Minecraft-like single and multi-agent environments for AI research. (Accepted at ICML 2025).
SustainDC is a set of Python environments for Data Center simulation and control using Heterogeneous Multi Agent Reinforcement Learning. Includes customizable environments for workload scheduling, cooling optimization, and battery management, with integration into Gymnasium.
Simple Grid Environment for Gymnasium
SustainCluster: A high-fidelity, open-source Gymnasium environment for benchmarking multi-objective, sustainable workload scheduling across geo-distributed data centers. Integrates real-world AI traces, energy/carbon/weather data, and detailed DC physics.
Autonomous driving episode generation for the Carla simulator in a gym environment. This framework makes it easy to create driving scenarios to train/test the agent.
A collection of RL gymnasium environments for learning to grasp 3D deformable objects.
This repository provides an OpenAI Gym-compatible environment for production scheduling tasks, designed to benchmark reinforcement learning agents in job shop and flow shop settings.
A Gymnasium Environment for the Job Shop Problem Using the Disjunctive Graph Approach.
A Gymnasium environment for simulating and training reinforcement learning agents on the BlueROV2 underwater vehicle.
Gym environments for manipulators based on crisp_py and ROS2: collect data and deploy policies on real ROS2 enabled manipulators.
Environments and various utilities for reinforcement learning with C++
Code for my Master's thesis, game theory for adversarial autonomous vehicle platooning scenarios
Automated stock trading strategy using deep reinforcement learning and recurrent neural networks
Implementation of DQN and DDQN algorithms for Playing Car Racing Game
Gymnasium environment to get started with deep reinforcement learning (DRL) using ROS 2 and Gazebo.
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