OpenAI Gym
169 papers with code • 14 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
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.
A Benchmark Environment Motivated by Industrial Control Problems
siemens/industrialbenchmark
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On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand.
Recurrent Predictive State Policy Networks
Predictive state policy networks consist of a recursive filter, which keeps track of a belief about the state of the environment, and a reactive policy that directly maps beliefs to actions, to maximize the cumulative reward.
Monte Carlo Tree Search for Asymmetric Trees
tmoer/mcts-t
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Asymmetric termination of search trees introduces a type of uncertainty for which the standard upper confidence bound (UCB) formula does not account.
Deep Reinforcement Learning for General Video Game AI
In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems.
Deep Reinforcement Learning with Feedback-based Exploration
We employ binary corrective feedback as a general and intuitive manner to incorporate human intuition and domain knowledge in model-free machine learning.
Towards Interactive Training of Non-Player Characters in Video Games
We propose to create such NPC behaviors interactively by training an agent in the target environment using imitation learning with a human in the loop.
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
marko-vasic/moet
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By training Mo\"ET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models.
