SOTAVerified

OpenAI Gym

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)

Papers

Showing 226–250 of 382 papers

TitleStatusHype
Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems—0
Reinforcement Learning for Robotics and Control with Active Uncertainty Reduction—0
Reinforcement Learning using Guided Observability—0
Relative Importance Sampling for off-Policy Actor-Critic in Deep Reinforcement Learning—0
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning—0
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations—0
Rethinking Population-assisted Off-policy Reinforcement Learning—0
Robustness Evaluation of Offline Reinforcement Learning for Robot Control Against Action Perturbations—0
Sample-based Distributional Policy Gradient—0
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing—0
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research—0
SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Models—0
Sepsis World Model: A MIMIC-based OpenAI Gym "World Model" Simulator for Sepsis Treatment—0
Sequential Learning of Movement Prediction in Dynamic Environments using LSTM Autoencoder—0
Session-Level Dynamic Ad Load Optimization using Offline Robust Reinforcement Learning—0
SIMILE: Introducing Sequential Information towards More Effective Imitation Learning—0
skrl: Modular and Flexible Library for Reinforcement Learning—0
Soft Actor-Critic with Inhibitory Networks for Faster Retraining—0
State Distribution-aware Sampling for Deep Q-learning—0
Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning—0
Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning—0
STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation—0
Structured Evolution with Compact Architectures for Scalable Policy Optimization—0
Sufficient Exploration for Convex Q-learning—0
SURREAL-System: Fully-Integrated Stack for Distributed Deep Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MEowAverage Return6,586.33—Unverified
2TD3Average Return5,942.55—Unverified
3SACAverage Return5,208.09—Unverified
4DDPGAverage Return1,712.12—Unverified
5PPOAverage Return608.97—Unverified
#ModelMetricClaimedVerifiedStatus
1SACAverage Return15,836.04—Unverified
2DDPGAverage Return14,934.86—Unverified
3TD3Average Return12,026.73—Unverified
4MEowAverage Return10,981.47—Unverified
5PPOAverage Return6,006.11—Unverified
#ModelMetricClaimedVerifiedStatus
1MEowAverage Return3,332.99—Unverified
2TD3Average Return3,319.98—Unverified
3SACAverage Return2,882.56—Unverified
4DDPGAverage Return1,290.24—Unverified
5PPOAverage Return790.77—Unverified
#ModelMetricClaimedVerifiedStatus
1MEowAverage Return6,923.22—Unverified
2SACAverage Return6,211.5—Unverified
3PPOAverage Return925.89—Unverified
4TD3Average Return198.44—Unverified
5DDPGAverage Return139.14—Unverified
#ModelMetricClaimedVerifiedStatus
1SACAverage Return5,745.27—Unverified
2MEowAverage Return5,526.66—Unverified
3DDPGAverage Return2,994.54—Unverified
4PPOAverage Return2,739.81—Unverified
5TD3Average Return2,612.74—Unverified
#ModelMetricClaimedVerifiedStatus
1TLAMean Reward5,163.54—Unverified
2AWRMean Reward5,067—Unverified
#ModelMetricClaimedVerifiedStatus
1Orthogonal decision treeAverage Return500—Unverified
2Oblique decision treeAverage Return500—Unverified
#ModelMetricClaimedVerifiedStatus
1TLAMean Reward9,571.99—Unverified
2AWRMean Reward9,136—Unverified
#ModelMetricClaimedVerifiedStatus
1TLAMean Reward3,458.22—Unverified
2AWRMean Reward3,405—Unverified
#ModelMetricClaimedVerifiedStatus
1Oblique decision treeAverage Return272.14—Unverified
2AWRAverage Return229—Unverified
#ModelMetricClaimedVerifiedStatus
1Orthogonal decision treeAverage Return-101.72—Unverified
2Oblique decision treeAverage Return-106.02—Unverified
#ModelMetricClaimedVerifiedStatus
1TLA with Hierarchical Reward FunctionsMean Reward-125.02—Unverified
2TLAMean Reward-154.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AWRMean Reward5,813—Unverified
2TLAMean Reward3,878.41—Unverified
#ModelMetricClaimedVerifiedStatus
1AWRAverage Return4,996—Unverified
#ModelMetricClaimedVerifiedStatus
1TLAMean Reward9,356.67—Unverified
#ModelMetricClaimedVerifiedStatus
1TLAMean Reward1,000—Unverified
#ModelMetricClaimedVerifiedStatus
1TLAMean Reward93.88—Unverified