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
Implicit Sensing in Traffic Optimization: Advanced Deep Reinforcement Learning Techniques—0
ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints—0
Implicit Two-Tower Policies—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 Learning of Koopman Representation for Control—0
Deep Reinforcement Learning for ESG financial portfolio management—0
Affine Transport for Sim-to-Real Domain Adaptation—0
Behavior Cloning in OpenAI using Case Based Reasoning—0
ReaCritic: Large Reasoning Transformer-based DRL Critic-model Scaling For Heterogeneous Networks—0
Adversarial Exploration Strategy for Self-Supervised Imitation Learning—0
Attention Loss Adjusted Prioritized Experience Replay—0
A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning—0
Benchmarking Algorithms from Machine Learning for Low-Budget Black-Box Optimization—0
Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning—0
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets—0
Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies—0
Adversarial Body Shape Search for Legged Robots—0
Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym—0
Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization—0
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning—0
Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning—0
Learning Gaussian Policies from Corrective Human Feedback—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
Data Driven Control with Learned Dynamics: Model-Based versus Model-Free Approach—0
Curiosity-Driven Experience Prioritization via Density Estimation—0
Towards Understanding Asynchronous Advantage Actor-critic: Convergence and Linear Speedup—0
CT-DQN: Control-Tutored Deep Reinforcement Learning—0
CrowdPlay: Crowdsourcing human demonstration data for offline learning in Atari games—0
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning—0
A Strategy-Oriented Bayesian Soft Actor-Critic Model—0
Advantage Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning—0
Correcting discount-factor mismatch in on-policy policy gradient methods—0
Hypothesis Driven Coordinate Ascent for Reinforcement Learning—0
Illuminating Spaces: Deep Reinforcement Learning and Laser-Wall Partitioning for Architectural Layout Generation—0
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control—0
HomeLabGym: A real-world testbed for home energy management systems—0
Controlling an Inverted Pendulum with Policy Gradient Methods-A Tutorial—0
A Closed-Loop Multi-perspective Visual Servoing Approach with Reinforcement Learning—0
Human AI interaction loop training: New approach for interactive reinforcement learning—0
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces—0
AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning—0
gym-saturation: Gymnasium environments for saturation provers (System description)—0
A Dual Memory Structure for Efficient Use of Replay Memory in Deep Reinforcement Learning—0
Decision-Making in Reinforcement Learning—0
HoME: a Household Multimodal Environment—0
Hybrid Policies Using Inverse Rewards for Reinforcement Learning—0
Imaginary Hindsight Experience Replay: Curious Model-based Learning for Sparse Reward Tasks—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