https://github.com/glassroom/heinsen_attention
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Papers with Code 아카이브
General Reinforcement Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/glinscott/leela-chess
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/goodls-cs/leela-zero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/google-deepmind/acme
Acme: A Research Framework for Distributed Reinforcement Learning
https://github.com/google-deepmind/open_spiel
https://github.com/google/brain-tokyo-workshop
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
https://github.com/gtagency/beta-zero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/gwthomas/iql-pytorch
https://github.com/haosulab/ManiSkill-Learn
https://github.com/helpstonex/crazyara
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/higcompetition/tournament
https://github.com/howuhh/faster-trajectory-transformer
Offline Reinforcement Learning as One Big Sequence Modeling Problem
https://github.com/howuhh/sac-n-jax
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
https://github.com/huggingface/transformers
https://github.com/huihanl/rl_unplugged
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
https://github.com/iclavera/learning_to_adapt
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
https://github.com/idthanm/env_build
https://github.com/ikostrikov/cql-results
https://github.com/ikostrikov/rlpd
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
https://github.com/intenseG/BSK
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/jasonrobwebster/alphazero-clone
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/jerrodparker20/adaptive-transformers-in-rl
https://github.com/jesbu1/carl
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
https://github.com/jhejna/cpl
Direct Preference-based Policy Optimization without Reward Modeling
https://github.com/jingwu6/handful-of-trials-in-pytorch
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/jonah-chen/alphazero-guerzhoy
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/jonahs99/baby-zero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/jonathan-laurent/AlphaZero.jl
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/junming-yang/mopo
https://github.com/junnyu/paddle_reformer
https://github.com/jxu43/replication-mbpo
https://github.com/k-lombard/CS4641_Project
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/k-lombard/Deep-Learning-Chess-AI
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/karim-abdel/fact
https://github.com/koulanurag/opcc
https://github.com/kpertsch/d4rl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.