Papers with Code 아카이브
Reinforcement Learning (RL)
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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/rddy/ReQueST
Reward learning from human preferences and demonstrations in Atari
https://github.com/rddy/deepassist
https://github.com/rebekkaorth/robotics_pick_n_place_masterproject
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning
https://github.com/rehoss/lbmpc_semimarkov
An Experimental Design Perspective on Model-Based Reinforcement Learning
https://github.com/reiniscimurs/TD3_Separate_Action
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/rejuvyesh/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/remusionita/flow_rl
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/renweiya/RFQ-RFAC-Represented-Value-Function-Approach-for-Large-Scale-Multi-Agent-Reinforcement-Learning
Represented Value Function Approach for Large Scale Multi Agent Reinforcement Learning
https://github.com/reservoirpy/reservoirpy
https://github.com/resuldagdanov/DeFIX
https://github.com/resuldagdanov/self-improving-RL
https://github.com/rgilman33/carlita
Leveraging Procedural Generation to Benchmark Reinforcement Learning
https://github.com/rgreenblatt/ai_alignment_readings
https://github.com/richardehughes/chnflow
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/richardrl/cartpole-request-for-research
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/richardrl/rlkit-relational
Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning
https://github.com/richemslie/galvanise_zero
https://github.com/rickyegl/BeyondTheRainbowICLR
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC
https://github.com/rickyegl/BeyondTheRainbowM
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC
https://github.com/rigetti/gym-forest
Automated quantum programming via reinforcement learning for combinatorial optimization
https://github.com/rikluost/RL_DQN_Pong
https://github.com/rishabhsahrawat/textbasedgameusingNeuralNet
Language Understanding for Text-based Games Using Deep Reinforcement Learning
https://github.com/rishavb123/MineRL
https://github.com/riveSunder/harli_learning
https://github.com/riveSunder/moving_in_morley
https://github.com/rk1998/robot-sac
https://github.com/rle-foundation/hsuanwu
RLLTE: Long-Term Evolution Project of Reinforcement Learning
https://github.com/rll-research/cic
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery
https://github.com/rll-research/rune
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
https://github.com/rll/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rlworkgroup/garage
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rmrafailov/lompo
Offline Reinforcement Learning from Images with Latent Space Models
https://github.com/robbiejones96/RubiksSolver
https://github.com/robintyh1/icml2021-pengqlambda
https://github.com/roboticist-by-day/Rainbow_DQN
Rainbow: Combining Improvements in Deep Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.