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Reinforcement Learning

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보존된 평가 기록

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코드

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

https://github.com/mickelliu/selfplay-redteaming

OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

https://github.com/microsoft/FQF

Fully Parameterized Quantile Function for Distributional Reinforcement Learning

https://github.com/microsoft/affect_based_intrinsic_rewards_for_learning_representations

Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards

https://github.com/microsoft/lightatac

Adversarially Trained Actor Critic for Offline Reinforcement Learning

https://github.com/microsoft/maro

A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network

https://github.com/microsoft/med-deadend

Deep Reinforcement Learning with Double Q-learning

https://github.com/microsoft/mttl

Combining Modular Skills in Multitask Learning

https://github.com/microsoft/syntheseus

It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design

https://github.com/mightypirate1/DRL-Tetris

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/mightypirate1/PPO_homebrew

Proximal Policy Optimization Algorithms

https://github.com/miguelsvasco/multimodal-atari-games

Playing Games in the Dark: An approach for cross-modality transfer in reinforcement learning

https://github.com/mikehuisman/revisiting-learned-optimizers

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/mila-iqia/climate-cooperation-competition

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

https://github.com/mila-iqia/ddxplus

Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and Classification

https://github.com/mila-udem/babyai

BabyAI 1.1

https://github.com/milesgep/Quantum-Reinforcement-Learning

Quantum reinforcement learning

https://github.com/milutter/deep_lagrangian_networks

Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems

https://github.com/milutter/value_iteration

Value Iteration in Continuous Actions, States and Time

https://github.com/mimikaan/Attention-Model

Recurrent Models of Visual Attention

https://github.com/min942773/parlai_wandb

ParlAI: A Dialog Research Software Platform

https://github.com/minbeomkim/CriticControl

Critic-Guided Decoding for Controlled Text Generation

https://github.com/mindspore-ai/models/tree/master/research/cv/adelaide_ea

Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells

https://github.com/mindspore-ai/models/tree/master/research/cv/pnasnet

Progressive Neural Architecture Search

https://github.com/mindspore-ai/models/tree/master/research/hpc/deepbsde

Solving high-dimensional partial differential equations using deep learning

https://github.com/mindspore-courses/Deep-Reinforcement-Learning-Algorithms-with-MindSpore

Playing Atari with Deep Reinforcement Learning

https://github.com/mindspore-courses/Rainbow-MindSpore

Deep Reinforcement Learning with Double Q-learning

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/pnasnet.py

Progressive Neural Architecture Search

https://github.com/minerllabs/minerl

MineRL: A Large-Scale Dataset of Minecraft Demonstrations

https://github.com/minfanzhang/noisy-K-FAC_added_flipout

Noisy Natural Gradient as Variational Inference

https://github.com/mingyip/Rainbow-Reinforcement-Learning-For-Chess-Games

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/minhphd/PyDreamerV1

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/minosworld/minos

MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments

https://github.com/minseop-aitrics/FewshotLearning

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/miralab-ustc/rl-spf

Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?

https://github.com/miroblog/tf_deep_rl_trader

Proximal Policy Optimization Algorithms

https://github.com/mit-acl/cadrl_ros

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

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