Papers with Code 아카이브

Offline RL

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

https://github.com/BY571/Implicit-Q-Learning

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/DHDev0/Muzero-unplugged

Online and Offline Reinforcement Learning by Planning with a Learned Model

https://github.com/DanielTakeshi/DCUR

DCUR: Data Curriculum for Teaching via Samples with Reinforcement Learning

https://github.com/Delavari-Alireza/Decision_Transformer_Sequence-Modeling_Deep_Reinforcement_Learning

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/DrJupiter/Deep-Reinforcement-Rubiks-Cube-Solver

Revisiting Fundamentals of Experience Replay

https://github.com/EugenHotaj/pytorch-generative/blob/master/pytorch_generative/nn/attention.py

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

https://github.com/HoldenB/TD3-BC-from-scratch

A Minimalist Approach to Offline Reinforcement Learning

https://github.com/HzcIrving/DecisionTransformer_StepbyStep

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/LAS1520/Gato-A-Generalist-Agent

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/Manchery/iql-pytorch

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/MindCode-4/code-2/tree/main/qdqbert

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

https://github.com/MindCode-4/code-3/tree/main/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/MindCode-4/code-3/tree/main/efficientformer

Reformer: The Efficient Transformer

https://github.com/MindCode-4/code-4/tree/main/qdqbert

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

https://github.com/MohammadrezaNakhaei/FQL

Flow Q-Learning

https://github.com/RamiSketcher/decision-transformer-ammi

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/Rick-McCoy/Reformer-pytorch

Reformer: The Efficient Transformer

https://github.com/SwapnilPande/MOReL

MOReL : Model-Based Offline Reinforcement Learning

https://github.com/Yihao-Sun/pytorch-mopo

MOPO: Model-based Offline Policy Optimization

https://github.com/adamjelley/efficientofflinerl

Revisiting the Minimalist Approach to Offline Reinforcement Learning

https://github.com/ai4ai-lab/factored-action-spaces-for-ope

Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare

https://github.com/anair13/bullet-manipulation-affordances

Offline Meta-Reinforcement Learning with Online Self-Supervision

https://github.com/anuragajay/d4rl

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/bainro/mcai

DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs

https://github.com/buseskorkmaz/fms-at-work

Offline RL for Natural Language Generation with Implicit Language Q Learning

https://github.com/cannylab/casual_overhypotheses

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/charleshsc/HarmoDT

Q-value Regularized Transformer for Offline Reinforcement Learning

https://github.com/charleshsc/qt

Q-value Regularized Transformer for Offline Reinforcement Learning

https://github.com/chinganc/lightatac

Adversarially Trained Actor Critic for Offline Reinforcement Learning

https://github.com/colingwuyu/HedgingBox

Acme: A Research Framework for Distributed Reinforcement Learning

https://github.com/davidsvy/cosformer-pytorch

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

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/digirl-agent/digirl

DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning

https://github.com/div99/xql

Extreme Q-Learning: MaxEnt RL without Entropy

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.