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OpenAI Gym

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하위 태스크: 1
평가표: 17
보존된 평가 기록: 45

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)

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://bitbucket.org/RLinRL/intelligenttrainerpublic

공식 코드

Intelligent Trainer for Model-Based Reinforcement Learning

https://github.com/BigDataWUR/crop-gym

공식 코드

CropGym: a Reinforcement Learning Environment for Crop Management

https://github.com/BlackHC/mdp

공식 코드

MDP environments for the OpenAI Gym

https://github.com/BlueRiverTech/quanser-openai-driver

공식 코드

Blue River Controls: A toolkit for Reinforcement Learning Control Systems on Hardware

https://github.com/Breakend/gym-extensions

공식 코드

Benchmark Environments for Multitask Learning in Continuous Domains

https://github.com/CBCodeHub/Mining-Gym

공식 코드

Mining-Gym: A Configurable RL Benchmarking Environment for Truck Dispatch Scheduling

https://github.com/CORE-Robotics-Lab/ProLoNets

공식 코드

Neural-encoding Human Experts' Domain Knowledge to Warm Start Reinforcement Learning

https://github.com/CausalRL/DRL

공식 코드

Deconfounding Reinforcement Learning in Observational Settings

https://github.com/ChienFeng-hub/meow

공식 코드

Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow

https://github.com/DamianoBrunori/MultiUAV-OpenAIGym

공식 코드

A Reinforcement Learning Environment for Multi-Service UAV-enabled Wireless Systems

https://github.com/GhadaSokar/Dynamic-Sparse-Training-for-Deep-Reinforcement-Learning

공식 코드

Dynamic Sparse Training for Deep Reinforcement Learning

https://github.com/HDDLGym/HDDLGym

공식 코드

HDDLGym: A Tool for Studying Multi-Agent Hierarchical Problems Defined in HDDL with OpenAI Gym

https://github.com/Hananel-Hazan/bindsnet

공식 코드

BindsNET: A machine learning-oriented spiking neural networks library in Python

https://github.com/IanYangChina/pybullet_multigoal_gym

공식 코드

An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet

https://github.com/IanYangChina/taros2021codes

공식 코드

An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet

https://github.com/MLResearchAtOSRAM/tmm_fast

공식 코드

TMM-Fast: A Transfer Matrix Computation Package for Multilayer Thin-Film Optimization

https://github.com/MaxSobolMark/OOO

공식 코드

Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias

https://github.com/MichaelAllen1966/learninghospital

공식 코드

Integrating Deep Reinforcement Learning Networks with Health System Simulations

https://github.com/MichaelAllen1966/qambo

공식 코드

Developing an OpenAI Gym-compatible framework and simulation environment for testing Deep Reinforcement Learning agents solving the Ambulance Location Problem

https://github.com/MiguelAguilera/Adaptation-to-criticality-through-organizational-invariance

공식 코드

Adaptation to criticality through organizational invariance in embodied agents

https://github.com/MiguelAguilera/Criticality-as-It-Could-Be

공식 코드

Criticality as It Could Be: organizational invariance as self-organized criticality in embodied agents

https://github.com/Rajesh-Siraskar/Reinforcement-Learning-for-Control-of-Valves

공식 코드

Reinforcement Learning for Control of Valves

https://github.com/Spiegeldondi/A-Hybrid-Quantum-Classical-Framework-for-Reinforcement-Learning-of-Atari-Games

공식 코드

A quantum-classical reinforcement learning model to play Atari games

https://github.com/TofaraMoyo/Heirachical-Reward-Functions

공식 코드

Creating Hierarchical Dispositions of Needs in an Agent

https://github.com/Xingyu-Lin/softgym

공식 코드

SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation

https://github.com/YijiongLin/ITER_KER_GER

공식 코드

Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement Learning

https://github.com/aaronsnoswell/unimodal-irl

공식 코드

Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms

https://github.com/acrobot-buaa/robotdiffuse

공식 코드

RobotDiffuse: Motion Planning for Redundant Manipulator based on Diffusion Model

https://github.com/andycao1125/mambadm

공식 코드

Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement Learning

https://github.com/aslanides/aixijs

공식 코드

AIXIjs: A Software Demo for General Reinforcement Learning

https://github.com/ataitler/pyrddlgym

공식 코드

pyRDDLGym: From RDDL to Gym Environments

https://github.com/automl/learning_environments

공식 코드

Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies

https://github.com/automl/mdp-playground

공식 코드

MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning

https://github.com/az79nefy/OrdinalRL

공식 코드

Deep Ordinal Reinforcement Learning

https://github.com/baturaysaglam/dase

공식 코드

Safe and Robust Experience Sharing for Deterministic Policy Gradient Algorithms

https://github.com/bluecontra/AAAI2021-VDFP

공식 코드

Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.