OpenAI Gym

175 papers with code • 17 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)

Libraries

Use these libraries to find OpenAI Gym models and implementations
5 papers
646
4 papers
629
See all 21 libraries.

Subtasks


Most implemented papers

Proximal Policy Optimization Algorithms

labmlai/annotated_deep_learning_paper_implementations 20 Jul 2017

We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent.

Continuous control with deep reinforcement learning

ray-project/ray 9 Sep 2015

We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain.

Addressing Function Approximation Error in Actor-Critic Methods

sfujim/TD3 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.

Decision Transformer: Reinforcement Learning via Sequence Modeling

kzl/decision-transformer NeurIPS 2021

In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling.

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

google/trax 1 Oct 2019

In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines.

Deep Recurrent Q-Learning for Partially Observable MDPs

marload/DeepRL-TensorFlow2 23 Jul 2015

Deep Reinforcement Learning has yielded proficient controllers for complex tasks.

Deep Reinforcement Learning for Playing 2.5D Fighting Games

elvisyjlin/lf2gym 5 May 2018

Deep reinforcement learning has shown its success in game playing.

Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation

mpatacchiola/imujoco 23 Jun 2023

Despite its simplicity this baseline is competitive with meta-learning methods on a variety of conditions and is able to imitate target policies trained on unseen variations of the original environment.