https://github.com/jaem-seo/AI_tokamak_control
Addressing Function Approximation Error in Actor-Critic Methods
Papers with Code 아카이브
OpenAI Gym
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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/jakegrigsby/deep_control/blob/master/deep_control/ddpg.py
https://github.com/jakegrigsby/deep_control/blob/master/deep_control/grac.py
https://github.com/jakegrigsby/deep_control/blob/master/deep_control/sac.py
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/jakegrigsby/deep_control/blob/master/deep_control/td3.py
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/jcwleo/curiosity-driven-exploration-pytorch
https://github.com/jcwleo/random-network-distillation-pytorch
https://github.com/jeapostrophe/monaco
https://github.com/jfpettit/flare
https://github.com/jhare96/reinforcement-learning
https://github.com/jongornet14/HyperController
https://github.com/jsztompka/MultiAgent-PPO
https://github.com/jsztompka/PPO-demo
https://github.com/jturner65/Getup-DartEnv
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/juliusfrost/Research-Paper-Implementations
https://github.com/jvidals09/Decentralized-and-multi-agent-control-of-Franka-Emika-Panda-robot-in-continuous-task-execution
https://github.com/jw1401/PPO-Tensorflow-2.0
https://github.com/jyqhahah/rl_maddpg_matd3
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/kairproject/kair_algorithms_draft
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/kdally/fault-tolerant-flight-control-drl
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/keep9oing/DRQN-Pytorch-CartPole-v1
https://github.com/kevslinger/dtqn
https://github.com/khansel01/nes-npg
https://github.com/krasing/DRLearningCollaboration
https://github.com/krasing/DRLearningContinuousControl
https://github.com/ku2482/discor.pytorch
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/ku2482/gail-airl-ppo.pytorch
https://github.com/ku2482/rljax
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/ku2482/rltorch
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/ku2482/soft-actor-critic.pytorch
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/kushagra06/DDPG
https://github.com/kushagra06/SAC
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/lanqingli1993/focal-iclr
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/lava-lab/cool-mc
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model Checking
https://github.com/lava-lab/mc_pia
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model Checking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.