Papers with Code 아카이브
OpenAI Gym
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/tomsilver/pddlgym
공식 코드https://github.com/toni-sm/skrl
공식 코드skrl: Modular and Flexible Library for Reinforcement Learning
https://github.com/ucbtns/dai
공식 코드https://github.com/upb-lea/gym-electric-motor
공식 코드Towards a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control
https://github.com/utiasDSL/gym-pybullet-drones
공식 코드https://github.com/vadim0x60/cibi
공식 코드https://github.com/vadim0x60/cibi-experiments
공식 코드Neurogenetic Programming Framework for Explainable Reinforcement Learning
https://github.com/vivianchen98/inverse_mdpgame
공식 코드Soft-Bellman Equilibrium in Affine Markov Games: Forward Solutions and Inverse Learning
https://github.com/vtnsisdd/rfrl-gym
공식 코드RFRL Gym: A Reinforcement Learning Testbed for Cognitive Radio Applications
https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/rpo_continuous_action.py
공식 코드https://github.com/wensun/Imitation-Learning-from-Observation
공식 코드Provably Efficient Imitation Learning from Observation Alone
https://github.com/wyhwhy/Amortized-Variational-DQN
공식 코드https://github.com/xiangyuan-zhang/controlgym
공식 코드Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms
https://github.com/xjtuai/histogym
공식 코드HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis
https://github.com/yuanggeng/bridging-dimensions
공식 코드Bridging Dimensions: Confident Reachability for High-Dimensional Controllers
https://github.com/yuguangyue/CARSM
공식 코드https://github.com/zhang-zengjie/code_2023_iecon_shaping_wu
공식 코드Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving
https://github.com/zheng0428/more_
공식 코드MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic Spaces
https://github.com/zhougroup/IDAC
공식 코드https://gitlab.com/leocus/ge_q_dts
공식 코드https://bitbucket.org/act-lab/release
https://github.com/09jvilla/CS234_gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/1110589721/openia-gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/170928/-Review-Generative-Adversarial-Imitation-Learning
https://github.com/170928/-Review-Proximal-Policy-Optimization-Algorithms
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/DDAG
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/decision_transformer
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/2mawi2/master-thesis-experiments
https://github.com/AIDefender/MyDiscor
https://github.com/ASzot/ppo-pytorch
https://github.com/AcutronicRobotics/gym-gazebo2
gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo
https://github.com/AgrawalAmey/safe-explorer
https://github.com/AlexandraFilimokhina/MountainCarConrinious_DDPG
https://github.com/Amadeus979/decision-transformer
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/AmmarFayad/Behavioral-Actor-Critic
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.