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 251–300 of 382 papers

TitleStatusHype
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control—0
Correcting discount-factor mismatch in on-policy policy gradient methods—0
CrowdPlay: Crowdsourcing human demonstration data for offline learning in Atari games—0
CT-DQN: Control-Tutored Deep Reinforcement Learning—0
Curiosity-Driven Experience Prioritization via Density Estimation—0
Data Driven Control with Learned Dynamics: Model-Based versus Model-Free Approach—0
Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies—0
Deep Learning of Koopman Representation for Control—0
Deep Q Learning from Dynamic Demonstration with Behavioral Cloning—0
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment—0
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents—0
Deep Reinforcement Learning for ESG financial portfolio management—0
Deep Reinforcement Learning with Mixed Convolutional Network—0
Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning—0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention—0
Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization—0
Direct Mutation and Crossover in Genetic Algorithms Applied to Reinforcement Learning Tasks—0
Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning—0
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems—0
Distributionally Robust Statistical Verification with Imprecise Neural Networks—0
Double A3C: Deep Reinforcement Learning on OpenAI Gym Games—0
DQN with model-based exploration: efficient learning on environments with sparse rewards—0
DriverGym: Democratising Reinforcement Learning for Autonomous Driving—0
Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization—0
EasyRL: A Simple and Extensible Reinforcement Learning Framework—0
Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks—0
Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning—0
Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms—0
Enhancing Privacy and Security of Autonomous UAV Navigation—0
Error Controlled Actor-Critic Method to Reinforcement Learning—0
Evading Web Application Firewalls with Reinforcement Learning—0
Evolutionary Selective Imitation: Interpretable Agents by Imitation Learning Without a Demonstrator—0
Evolving Neural Networks in Reinforcement Learning by means of UMDAc—0
EVO-RL: Evolutionary-Driven Reinforcement Learning—0
Exact and Cost-Effective Automated Transformation of Neural Network Controllers to Decision Tree Controllers—0
Experience Replay More When It's a Key Transition in Deep Reinforcement Learning—0
Exploration and preference satisfaction trade-off in reward-free learning—0
Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design—0
Extended Radial Basis Function Controller for Reinforcement Learning—0
FuzzerGym: A Competitive Framework for Fuzzing and Learning—0
GeneSys: Enabling Continuous Learning through Neural Network Evolution in Hardware—0
Graph Decision Transformer—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
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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