Papers with Code 아카이브
General Reinforcement Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 6
- 보존된 평가 기록
- 12
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kyle-gao/TF_Transformer
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
https://github.com/l1t1/lz19
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/langorou/vamperouge
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/leela-zero/leela-zero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/leelachesszero/lczero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/linhlpv/Offline-and-OfflineOnline-RL
https://github.com/lions-epfl/lion
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
https://github.com/lucashueda/long_sentence_transformer
https://github.com/lucidrains/compressive-transformer-pytorch
https://github.com/lucidrains/linear-attention-transformer
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
https://github.com/lucidrains/performer-pytorch
https://github.com/lucidrains/reformer-pytorch
https://github.com/lucys0/HRL
https://github.com/m-serra/action-inference-for-video-prediction-benchmarking
https://github.com/mahaitongdae/safe_exp_env
https://github.com/manantomar/video-occupancy-models
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
https://github.com/maohangyu/TIT_open_source
PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning
https://github.com/mehdimashayekhi/Some-RL-Implementation
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/mengyangbai/CchessGo
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/michaelnny/alpha_zero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/microsoft/lightatac
Adversarially Trained Actor Critic for Offline Reinforcement Learning
https://github.com/microsoft/orchestrated-value-mapping
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
https://github.com/ml-jku/l2m
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/mpatacchiola/imujoco
https://github.com/mwalton/openspiel_benchmarks
https://github.com/nagaban2/nn_dynamics
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/nakamotoo/v-gps
Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance
https://github.com/natolambert/dynamicslearn
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/ngoodger/nle-language-wrapper
https://github.com/nicklashansen/tdmpc2
TD-MPC2: Scalable, Robust World Models for Continuous Control
https://github.com/nikhilbarhate99/min-decision-transformer
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/niutrans/gram
https://github.com/odokumaci/rainbow-unity-obstacle-tower-challenge
Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning
https://github.com/opendilab/DI-engine/blob/main/ding/policy/td3_bc.py
https://github.com/opendilab/LightZero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.