Papers with Code 아카이브

General Reinforcement Learning

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보존된 평가 기록: 12

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/openpsi-project/realhf

ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models

https://github.com/optimization-and-machine-learning-lab/open_spiel

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/orrivlin/implicit-q-learning

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/pathway/alphaxos

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/pcchenxi/lapo-offlienrl

Latent-Variable Advantage-Weighted Policy Optimization for Offline RL

https://github.com/perrin-isir/afu

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/pervasive-ai-lab/nle-language-wrapper

Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

https://github.com/phixion/phixion

Reinforcement Learning: An Overview

https://github.com/pikaju/pikaju

Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision

https://github.com/plkmo/AlphaZero_Connect4

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/pokaxpoka/RAD_state_aug

Benchmarking Model-Based Reinforcement Learning

https://github.com/pokaxpoka/rad_openaigym

Benchmarking Model-Based Reinforcement Learning

https://github.com/polixir/OfflineRL

MOPO: Model-based Offline Policy Optimization

https://github.com/probabilistic-ml/pirl

Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search

https://github.com/pwc-1/Paper-10/tree/main/transformer

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

https://github.com/pwc-1/Paper-8/tree/main/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/pytorch/rl/tree/main/examples/cql

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/pytorch/rl/tree/main/examples/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/pytorch/rl/tree/main/examples/iql

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/qinwang/leela-zero

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/qmaai/open_spiel

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/quanvuong/handful-of-trials-pytorch

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

https://github.com/rahulpandeycs/openspiel-games

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/ray-project/ray/tree/master/rllib

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/rehoss/lbmpc_semimarkov

An Experimental Design Perspective on Model-Based Reinforcement Learning

https://github.com/resuldagdanov/DeFIX

DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving

https://github.com/rlacombe/adsorbrl

Acme: A Research Framework for Distributed Reinforcement Learning

https://github.com/rmrafailov/lompo

Offline Reinforcement Learning from Images with Latent Space Models

https://github.com/ronuchit/GLIB-AAAI-2021

GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling

https://github.com/rvl-lab-utoronto/HyperCRL

Continual Model-Based Reinforcement Learning with Hypernetworks

https://github.com/ryanp73/ChessAI

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/s-gupta/map-plan-baseline

Learning Exploration Policies for Navigation

https://github.com/sagpant/leela-zero

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/sai-dev/sai

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/saikrishna-1996/deep_pepper_chess

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/samhippie/q-game-agent

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.