Category: Synthetic

Benchmarking Offline Goal-Conditioned RL. No. of environments: 9

Simulating and optimizing odor-based target tracking strategies in partially observable settings. No. of environments: 1

Panda-Gym is a flexible simulation environment for the Panda robotical arm, offering six manipulation tasks with varying control modes and reward structures for testing reinforcement learning algorithms.. No. of environments: 34

A flexible, grid-based environment for studying decentralized, partially observable multi-agent pathfinding (PO-MAPF) problems.. No. of environments: 1

Autonomous navigation tasks with dynamic goals and obstacles, optimized using PPO.. No. of environments: 2

The Procgen Benchmark is a collection of 16 dynamic, procedurally generated environments that push the boundries of how quickly reinforcement learning agents can acquire generalizable skills in randomized scenarios.. No. of environments: 16

An enhanced Proximal Policy Optimization (PPO) algorythm implementation for efficient, robust on-policy sampling in continuous action spaces.. No. of environments: 51

PyFlyt is a flexible library for simulating UAVs in RL, supporting various drone, plane and rocket models and control modes with advanced wind physics and rendering capabilities.. No. of environments: 28

Quantum Circuit Synthesis Environment is a Gymnasium-based framework for RL, designed to optimize the creation of quantum circuits using generative machine learning models. No. of environments: 5

Gym/Gymnasium environments (Reinforcement Learning). No. of environments: 1