OpenAI Gym
161 papers with code • 9 benchmarks • 3 datasets
An open-source toolkit from OpenAI that implements several Reinforcement Learning benchmarks including: classic control, Atari, Robotics and MuJoCo tasks.
(Description by Evolutionary learning of interpretable decision trees)
(Image Credit: OpenAI Gym)
Benchmarks
These leaderboards are used to track progress in OpenAI Gym
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Libraries
Use these libraries to find OpenAI Gym models and implementations
OpenAI Gym
Industrial Benchmark
MO-Gymnasium
Most implemented papers
Reinforcement Learning with Augmented Data
MishaLaskin/rad
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NeurIPS 2020
To this end, we present Reinforcement Learning with Augmented Data (RAD), a simple plug-and-play module that can enhance most RL algorithms.
Reinforcement Learning with Quantum Variational Circuits
luthierman/quantum-research-colab
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This work explores the potential for quantum computing to facilitate reinforcement learning problems.
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models
Epidemiologists model the dynamics of epidemics in order to propose control strategies based on pharmaceutical and non-pharmaceutical interventions (contact limitation, lock down, vaccination, etc).
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers
We present Ecole, a new library to simplify machine learning research for combinatorial optimization.
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation
Further, we evaluate a variety of algorithms on these tasks and highlight challenges for reinforcement learning algorithms, including dealing with a state representation that has a high intrinsic dimensionality and is partially observable.
Reinforcement Learning for Control of Valves
Rajesh-Siraskar/Reinforcement-Learning-for-Control-of-Valves
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This paper is a study of reinforcement learning (RL) as an optimal-control strategy for control of nonlinear valves.
Neurogenetic Programming Framework for Explainable Reinforcement Learning
vadim0x60/cibi-experiments
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Automatic programming, the task of generating computer programs compliant with a specification without a human developer, is usually tackled either via genetic programming methods based on mutation and recombination of programs, or via neural language models.
Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control
Robotic simulators are crucial for academic research and education as well as the development of safety-critical applications.
An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet
This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine.
MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments
This paper is an initial endeavor to bridge the gap between powerful Deep Reinforcement Learning methodologies and the problem of exploration/coverage of unknown terrains.
