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 251–300 of 382 papers

TitleStatusHype
Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning—0
Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning—0
Teaching a Robot to Walk Using Reinforcement Learning—0
Towards Brain-inspired System: Deep Recurrent Reinforcement Learning for Simulated Self-driving Agent—0
Towards Characterizing Divergence in Deep Q-Learning—0
Towards Combining On-Off-Policy Methods for Real-World Applications—0
Towards Physically Safe Reinforcement Learning under Supervision—0
Traffic control using intelligent timing of traffic lights with reinforcement learning technique and real-time processing of surveillance camera images—0
Transferring Domain Knowledge with an Adviser in Continuous Tasks—0
Untangling Braids with Multi-agent Q-Learning—0
Utilizing Skipped Frames in Action Repeats via Pseudo-Actions—0
Value-Based Deep RL Scales Predictably—0
VisualEnv: visual Gym environments with Blender—0
Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog—0
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning—0
Zap Q-Learning With Nonlinear Function Approximation—0
Gym-preCICE: Reinforcement Learning Environments for Active Flow Control—0
Gym-saturation: an OpenAI Gym environment for saturation provers—0
gym-saturation: Gymnasium environments for saturation provers (System description)—0
HoME: a Household Multimodal Environment—0
HomeLabGym: A real-world testbed for home energy management systems—0
Human AI interaction loop training: New approach for interactive reinforcement learning—0
Hybrid Policies Using Inverse Rewards for Reinforcement Learning—0
Hypothesis Driven Coordinate Ascent for Reinforcement Learning—0
Illuminating Spaces: Deep Reinforcement Learning and Laser-Wall Partitioning for Architectural Layout Generation—0
Imaginary Hindsight Experience Replay: Curious Model-based Learning for Sparse Reward Tasks—0
Implementing Reinforcement Learning Algorithms in Retail Supply Chains with OpenAI Gym Toolkit—0
Implicit Sensing in Traffic Optimization: Advanced Deep Reinforcement Learning Techniques—0
Implicit Two-Tower Policies—0
Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym—0
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents—0
In Support of Over-Parametrization in Deep Reinforcement Learning: an Empirical Study—0
gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and GazeboCode0
Gym-Ignition: Reproducible Robotic Simulations for Reinforcement LearningCode0
A quantum-classical reinforcement learning model to play Atari gamesCode0
Control with adaptive Q-learningCode0
Continuous Control With Ensemble Deep Deterministic Policy GradientsCode0
HDDLGym: A Tool for Studying Multi-Agent Hierarchical Problems Defined in HDDL with OpenAI GymCode0
Decision Mamba ArchitecturesCode0
HistoGym: A Reinforcement Learning Environment for Histopathological Image AnalysisCode0
Continuous-action Reinforcement Learning for Playing Racing Games: Comparing SPG to PPOCode0
Safe and Robust Experience Sharing for Deterministic Policy Gradient AlgorithmsCode0
QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security GameCode0
Playing Games in the Dark: An approach for cross-modality transfer in reinforcement learningCode0
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous ControlCode0
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement LearningCode0
Mimicking Better by Matching the Approximate Action DistributionCode0
Constrained Policy Gradient Method for Safe and Fast Reinforcement Learning: a Neural Tangent Kernel Based ApproachCode0
Guaranteeing Control Requirements via Reward Shaping in Reinforcement LearningCode0
Project proposal: A modular reinforcement learning based automated theorem proverCode0
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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