OpenAI Gym
161 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
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.
Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias
MaxSobolMark/OOO
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Can we leverage offline RL to recover better policies from online interaction?
qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation
The goal of qgym is to connect the research fields of Artificial Intelligence (AI) with quantum compilation by abstracting parts of the process that are irrelevant to either domain.
Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning
cbellinger27/learning-when-to-observe-in-rl
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The action-perception cycle in RL, however, generally assumes that a measurement of the state of the environment is available at each time step without a cost.
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
Despite its simplicity this baseline is competitive with meta-learning methods on a variety of conditions and is able to imitate target policies trained on unseen variations of the original environment.
Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving
Reinforcement learning (RL) is an effective approach to motion planning in autonomous driving, where an optimal driving policy can be automatically learned using the interaction data with the environment.
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
In the field of reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked for environments with low-level states, such as physical control problems.
Signal Novelty Detection as an Intrinsic Reward for Robotics
In advanced robot control, reinforcement learning is a common technique used to transform sensor data into signals for actuators, based on feedback from the robot’s environment.
Neuroevolution of Recurrent Architectures on Control Tasks
Simple evolutionary algorithms have recently been shown to also be capable of optimizing deep neural network parameters, at times matching the performance of gradient-based techniques, e. g. in reinforcement learning settings.
Generative Adversarial Neuroevolution for Control Behaviour Imitation
There is a recent surge in interest for imitation learning, with large human video-game and robotic manipulation datasets being used to train agents on very complex tasks.
