OpenAI Gym
160 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
Addressing Function Approximation Error in Actor-Critic Methods
sfujim/TD3
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ICML 2018
In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies.
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
DartEnv/dart-env
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The purpose of this technical report is two-fold.
Decision Transformer: Reinforcement Learning via Sequence Modeling
kzl/decision-transformer
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NeurIPS 2021
In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling.
Deep Recurrent Q-Learning for Partially Observable MDPs
marload/DeepRL-TensorFlow2
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Deep Reinforcement Learning has yielded proficient controllers for complex tasks.
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
google/trax
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In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines.
Deep Reinforcement Learning for Playing 2.5D Fighting Games
Deep reinforcement learning has shown its success in game playing.
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
ruizhaogit/mep
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This objective encourages the agent to maximize the expected return, as well as to achieve more diverse goals.
TorchBeast: A PyTorch Platform for Distributed RL
TorchBeast is a platform for reinforcement learning (RL) research in PyTorch.
Implicit Distributional Reinforcement Learning
zhougroup/IDAC
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NeurIPS 2020
To improve the sample efficiency of policy-gradient based reinforcement learning algorithms, we propose implicit distributional actor-critic (IDAC) that consists of a distributional critic, built on two deep generator networks (DGNs), and a semi-implicit actor (SIA), powered by a flexible policy distribution.
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model Checking
This paper presents COOL-MC, a tool that integrates state-of-the-art reinforcement learning (RL) and model checking.
