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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://github.com/burakdmb/quadsim

공식 코드

QuadSim: A Quadcopter Rotational Dynamics Simulation Framework For Reinforcement Learning Algorithms

https://github.com/bva-bme/Constrained_Policy_Gradient

공식 코드

Constrained Policy Gradient Method for Safe and Fast Reinforcement Learning: a Neural Tangent Kernel Based Approach

https://github.com/bva-bme/Controlled_DQN

공식 코드

Deep Q-learning: a robust control approach

https://github.com/cair/rl

공식 코드

CaiRL: A High-Performance Reinforcement Learning Environment Toolkit

https://github.com/caltech-netlab/acnportal-experiments

공식 코드

ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research

https://github.com/cbellinger27/learning-when-to-observe-in-rl

공식 코드

Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning

https://github.com/cgrivera/ai-arena

공식 코드

The AI Arena: A Framework for Distributed Multi-Agent Reinforcement Learning

https://github.com/combra-lab/pop-spiking-deep-rl

공식 코드

Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control

https://github.com/continualai/avalanche-rl

공식 코드

Avalanche RL: a Continual Reinforcement Learning Library

https://github.com/crisbodnar/pderl

공식 코드

Proximal Distilled Evolutionary Reinforcement Learning

https://github.com/dcgym/iroko

공식 코드

Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic Control

https://github.com/declanoller/rwg-benchmark

공식 코드

Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing

https://github.com/dee0512/Temporally-Layered-Architecture

공식 코드

Optimizing Attention and Cognitive Control Costs Using Temporally-Layered Architectures

https://github.com/deepmind/pushworld

공식 코드

PushWorld: A benchmark for manipulation planning with tools and movable obstacles

https://github.com/dfki-ric-quantum/qdrl-turtlebot-env

공식 코드

Quantum Deep Reinforcement Learning for Robot Navigation Tasks

https://github.com/dfki-ric-quantum/qdrl-turtlebot-eval

공식 코드

Quantum Deep Reinforcement Learning for Robot Navigation Tasks

https://github.com/diambra/arena

공식 코드

DIAMBRA Arena: a New Reinforcement Learning Platform for Research and Experimentation

https://github.com/dimikout3/GeneralExplorationPolicy

공식 코드

MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments

https://github.com/dimikout3/MarsExplorer

공식 코드

MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments

https://github.com/drlinter/drlinter

공식 코드

Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach

https://github.com/ds4dm/ecole

공식 코드

Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers

https://github.com/duongnhatthang/llamagym

공식 코드

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM

https://github.com/ed2-paper/ed2

공식 코드

Continuous Control With Ensemble Deep Deterministic Policy Gradients

https://github.com/elvisyjlin/lf2gym

공식 코드

Deep Reinforcement Learning for Playing 2.5D Fighting Games

https://github.com/emi-group/evox

공식 코드

EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary Computation

https://github.com/ericlupy/isar_rep

공식 코드

Repairing Learning-Enabled Controllers While Preserving What Works

https://github.com/ethz-asl/navrep

공식 코드

NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments

https://github.com/fabiopardo/tonic

공식 코드

Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking

https://github.com/facebookresearch/CompilerGym

공식 코드

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

https://github.com/facebookresearch/mvfst-rl

공식 코드

MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions

https://github.com/facebookresearch/svg

공식 코드

On the model-based stochastic value gradient for continuous reinforcement learning

https://github.com/flowersteam/EpidemiOptim

공식 코드

EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

https://github.com/francescodelellis/reward-shaping-for-rl-based-control

공식 코드

Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning

https://github.com/frt03/pic

공식 코드

Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

https://github.com/google-research/google-research

공식 코드

SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Models

https://github.com/google/brax

공식 코드

Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation

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