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

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Showing 101–150 of 382 papers

TitleStatusHype
HistoGym: A Reinforcement Learning Environment for Histopathological Image AnalysisCode0
Adaptive Planning with Generative Models under Uncertainty—0
Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms—0
A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning—0
Traffic control using intelligent timing of traffic lights with reinforcement learning technique and real-time processing of surveillance camera images—0
Decision Mamba ArchitecturesCode0
SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory SystemsCode0
Off-OAB: Off-Policy Policy Gradient Method with Optimal Action-Dependent Baseline—0
Airlift Challenge: A Competition for Optimizing Cargo Delivery—0
Enhancing Privacy and Security of Autonomous UAV Navigation—0
HomeLabGym: A real-world testbed for home energy management systems—0
Noisy Spiking Actor Network for Exploration—0
QF-tuner: Breaking Tradition in Reinforcement Learning—0
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic SpacesCode0
Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization—0
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research—0
MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems—0
Decision Making in Non-Stationary Environments with Policy-Augmented SearchCode0
A Closed-Loop Multi-perspective Visual Servoing Approach with Reinforcement Learning—0
Investigating the Performance and Reliability, of the Q-Learning Algorithm in Various Unknown EnvironmentsCode0
Efficient Parallel Reinforcement Learning Framework using the Reactor ModelCode0
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations—0
Guaranteeing Control Requirements via Reward Shaping in Reinforcement LearningCode0
Bridging Dimensions: Confident Reachability for High-Dimensional ControllersCode0
Repairing Learning-Enabled Controllers While Preserving What WorksCode0
SDGym: Low-Code Reinforcement Learning Environments using System Dynamics ModelsCode0
Neural architecture impact on identifying temporally extended Reinforcement Learning tasks—0
Optimizing with Low Budgets: a Comparison on the Black-box Optimization Benchmarking Suite and OpenAI Gym—0
Implicit Sensing in Traffic Optimization: Advanced Deep Reinforcement Learning Techniques—0
gym-saturation: Gymnasium environments for saturation provers (System description)—0
Attention Loss Adjusted Prioritized Experience Replay—0
Distributionally Robust Statistical Verification with Imprecise Neural Networks—0
Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning—0
On Combining Expert Demonstrations in Imitation Learning via Optimal Transport—0
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing—0
Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement LearningCode0
Learning Environment Models with Continuous Stochastic Dynamics—0
Correcting discount-factor mismatch in on-policy policy gradient methods—0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
Deep Reinforcement Learning for ESG financial portfolio management—0
Mimicking Better by Matching the Approximate Action DistributionCode0
Active Inference in Hebbian Learning Networks—0
Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous DrivingCode0
Optimizing Attention and Cognitive Control Costs Using Temporally-Layered ArchitecturesCode0
Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning—0
Rethinking Population-assisted Off-policy Reinforcement Learning—0
Gym-preCICE: Reinforcement Learning Environments for Active Flow Control—0
Signal Novelty Detection as an Intrinsic Reward for RoboticsCode0
Exact and Cost-Effective Automated Transformation of Neural Network Controllers to Decision Tree Controllers—0
Causal Repair of Learning-enabled Cyber-physical Systems—0
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All datasetsAnt-v4HalfCheetah-v4Hopper-v4Humanoid-v4Walker2d-v4Ant-v2CartPole-v1HalfCheetah-v2Hopper-v2LunarLander-v2Mountain CarPendulum-v1

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