https://github.com/colindbrown/columbia-deep-learning-project
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
Papers with Code 아카이브
General Reinforcement Learning
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/colingwuyu/HedgingBox
Acme: A Research Framework for Distributed Reinforcement Learning
https://github.com/cosmobobak/ttt-ml
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/danielwillemsen/PendulumDemo
https://github.com/davidsvy/cosformer-pytorch
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
https://github.com/dazcona/obstacletower
Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning
https://github.com/deepmind/acme
Acme: A Research Framework for Distributed Reinforcement Learning
https://github.com/deepmind/alphastar
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
https://github.com/deepmind/enn_acme
Acme: A Research Framework for Distributed Reinforcement Learning
https://github.com/dhruvramani/Transformers-RL
https://github.com/digirl-agent/digirl
DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning
https://github.com/div99/xql
https://github.com/drblallo/open_spiel
https://github.com/edantoledo/stoix
https://github.com/enpasos/muzero
Online and Offline Reinforcement Learning by Planning with a Learned Model
https://github.com/eric-mitchell/macaw-min
Offline Meta-Reinforcement Learning with Advantage Weighting
https://github.com/ermongroup/CalibratedModelBasedRL
https://github.com/fabiopardo/tonic
https://github.com/facebookresearch/Horizon
https://github.com/facebookresearch/ReAgent
https://github.com/facebookresearch/fair_self_supervision_benchmark
https://github.com/facebookresearch/habitat-challenge
https://github.com/facebookresearch/minihack
The Effectiveness of World Models for Continual Reinforcement Learning
https://github.com/facebookresearch/salina
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/facebookresearch/slbo
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
https://github.com/facebookresearch/xformers
https://github.com/fantianwen/laalaz13E
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/fantianwen/leela13_training
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/farama-foundation/d4rl
https://github.com/foersterrobert/AlphaZero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/fusion-ml/trajectory-information-rl
An Experimental Design Perspective on Model-Based Reinforcement Learning
https://github.com/gaetanserre/lipo
https://github.com/gauthambekal93/hvac_continual_rl
https://github.com/gcp/leela-zero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/github-jnauta/pytorch-pne
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/gkassel/pyaixi
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.