OpenAI Gym
160 papers with code • 9 benchmarks • 3 datasets
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)
Benchmarks
These leaderboards are used to track progress in OpenAI Gym
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Libraries
Use these libraries to find OpenAI Gym models and implementations
OpenAI Gym
Industrial Benchmark
MO-Gymnasium
Latest papers
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic Spaces
zheng0428/more_
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Drawing upon the intuition that aligning different modalities to the same semantic embedding space would allow models to understand states and actions more easily, we propose a new perspective to the offline reinforcement learning (RL) challenge.
Decision Making in Non-Stationary Environments with Policy-Augmented Search
In this paper, we introduce \textit{Policy-Augmented Monte Carlo tree search} (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the environment.
RFRL Gym: A Reinforcement Learning Testbed for Cognitive Radio Applications
Index Terms-machine learning, reinforcement learning, wireless communications, dynamic spectrum access, OpenAI gym
Investigating the Performance and Reliability, of the Q-Learning Algorithm in Various Unknown Environments
As previously indicated, the majority of the conclusions of this study about the relationship between computation cost and environment and also dependability can be transferred to more sophisticated temporal difference-based algorithms because all methods are iterative.
Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations
Eventually, we analyze the learning behavior of the peers and observe their ability to rank the agents' performance within the study group and understand which agents give reliable advice.
Can language agents be alternatives to PPO? A Preliminary Empirical Study On OpenAI Gym
The formidable capacity for zero- or few-shot decision-making in language agents encourages us to pose a compelling question: Can language agents be alternatives to PPO agents in traditional sequential decision-making tasks?
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms
xiangyuan-zhang/controlgym
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This project serves the learning for dynamics & control (L4DC) community, aiming to explore key questions: the convergence of RL algorithms in learning control policies; the stability and robustness issues of learning-based controllers; and the scalability of RL algorithms to high- and potentially infinite-dimensional systems.
Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning
francescodelellis/reward-shaping-for-rl-based-control
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In addressing control problems such as regulation and tracking through reinforcement learning, it is often required to guarantee that the acquired policy meets essential performance and stability criteria such as a desired settling time and steady-state error prior to deployment.
Repairing Learning-Enabled Controllers While Preserving What Works
However, existing repair techniques do not preserve previously correct behaviors.
SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Models
google-research/google-research
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Understanding the long-term impact of algorithmic interventions on society is vital to achieving responsible AI.
