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평가표

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보존된 평가 기록

20

코드

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

https://github.com/yk7333/drnd

공식 코드

Exploration and Anti-Exploration with Distributional Random Network Distillation

https://github.com/yqpqry/sqog

공식 코드

Offline RL with Smooth OOD Generalization in Convex Hull and its Neighborhood

https://github.com/yuanying-cc/td3-rkl

공식 코드

TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets

https://github.com/zhaoyi11/adaptive_bc

공식 코드

Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning

https://github.com/zhendong-wang/diffusion-policies-for-offline-rl

공식 코드

Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

https://github.com/2023-MindSpore-1/ms-code-220/tree/main/pmdb

Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/Amadeus979/decision-transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

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

Offline Reinforcement Learning with Implicit Q-Learning

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

Decision Transformer: Reinforcement Learning via Sequence Modeling

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/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/MilkSilk/masters_thesis

IQ-Learn: Inverse soft-Q Learning for Imitation

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/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/adamjelley/efficientofflinerl

Revisiting the Minimalist Approach to Offline Reinforcement Learning

https://github.com/ankurhanda/implicit-bc-2d

Implicit Behavioral Cloning

https://github.com/anuragajay/d4rl

D4RL: Datasets for Deep Data-Driven Reinforcement 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/daniellawson9999/online-decision-transformer

Online Decision Transformer

https://github.com/dasgringuen/assetto_corsa_gym

Offline Reinforcement Learning with Implicit Q-Learning

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

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

https://github.com/div99/xql

Extreme Q-Learning: MaxEnt RL without Entropy

https://github.com/edmundmills/basalt-competition

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/evieq01/oodil

Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

https://github.com/facebookresearch/salina

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/facebookresearch/xformers

Rethinking Attention with Performers

https://github.com/farama-foundation/d4rl

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

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