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/baturaysaglam/RIS-MISO-Deep-Reinforcement-Learning
https://github.com/baturaysaglam/TCD3
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic Methods
https://github.com/baturaysaglam/ac-off-poc
https://github.com/baturaysaglam/la3p
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/bay3s/ppo-parallel
https://github.com/bay3s/ppo-rl
https://github.com/benevolentAI/guacamol_baselines
https://github.com/benpetit/cs379c
https://github.com/bentrevett/pytorch-rl
https://github.com/bidimensional/Tennis
https://github.com/biemann/Continuous-Control
https://github.com/bitterbloom/Reinforcement-Learning
https://github.com/bmeyers/VirtualMicrogridSegmentation
https://github.com/bonniesjli/PPO-Reacher_UnityML
https://github.com/bonniesjli/PPO_Reacher
https://github.com/cannylab/casual_overhypotheses
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/carlospurves/psxle
https://github.com/ccolas/rl_stats
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/chainer/chainerrl
https://github.com/cindycia/Atari-SAC-Discrete
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/cipher982/ppo-exploration
https://github.com/claudeHifly/BipedalWalker-v3
https://github.com/clwainwright/proximal_policy_optimization
https://github.com/core-robotics-lab/icct
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/coreylowman/rl_simply
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/corl-team/CORL
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/crazyleg/TD3-reacher
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/daggertye/GAN-Q-Learning
https://github.com/dan-lennox/ml-udacity-quadcopter-rl
https://github.com/danelee2601/rl-based-automatic-berthing
https://github.com/darkrush/meta_learn
https://github.com/dasgringuen/assetto_corsa_gym
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/davidsonic/self_brewed_gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/dchetelat/acer
https://github.com/decoderkurt/research_project_school_of_ai_2019
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.