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Reinforcement Learning

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

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

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

https://github.com/mit-biomimetics/orcagym

Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

https://github.com/mit-han-lab/ProxylessNAS

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

https://github.com/mit-han-lab/haq

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

https://github.com/mit-han-lab/haq-release

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

https://github.com/mit-han-lab/once-for-all

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

https://github.com/mit-ll/hanabi_anyplay

Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

https://github.com/mit-realm/neuriss

Proximal Policy Optimization Algorithms

https://github.com/miyosuda/unreal

Reinforcement Learning with Unsupervised Auxiliary Tasks

https://github.com/mizolotu/SmartExcavator

Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards

https://github.com/mjacar/pytorch-nec

Neural Episodic Control

https://github.com/mjiang9/_rlcard

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/mjiang9/rlcard-copy

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/mkachuee/DynamicFeatureAcquisition

Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data

https://github.com/mklissa/dceo

Eigenoption Discovery through the Deep Successor Representation

https://github.com/mkovalski/cs4995_cube

Solving the Rubik's Cube Without Human Knowledge

https://github.com/ml-group-sdu/diffail

Imitation Learning via Off-Policy Distribution Matching

https://github.com/ml-jku/l2m

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/ml-jku/reactive-exploration

History Compression via Language Models in Reinforcement Learning

https://github.com/mle-infrastructure/mle-hyperopt

Population Based Training of Neural Networks

https://github.com/mlii/mvrl

Multi-View Reinforcement Learning

https://github.com/mlisicki/neuralkernelbandits

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

https://github.com/mlvc-lab/Classification-NAS

DARTS: Differentiable Architecture Search

https://github.com/mmazuecos/reinact2021-impact-of-answers

Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat

https://github.com/mnecipkurt/tsg19

Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach

https://github.com/moduIo/Deep-Q-network

Deep Reinforcement Learning with Double Q-learning

https://github.com/mohit8935/Deep-Q-Learning-Paper

Deep Reinforcement Learning with Double Q-learning

https://github.com/mohith-sakthivel/rainbow_dqn

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/molomono/CartPole_Optimized_DDQN

Deep Reinforcement Learning with Double Q-learning

https://github.com/monoclecat/latent-conditioned-SAC

Discovering Diverse Solutions in Deep Reinforcement Learning by Maximizing State-Action-Based Mutual Information

https://github.com/monte-ai/expert-augmented-acktr

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/montrealrobotics/unsupervised-adr

Generating Automatic Curricula via Self-Supervised Active Domain Randomization

https://github.com/moonshotai/kimi-k1.5

Kimi k1.5: Scaling Reinforcement Learning with LLMs

https://github.com/morikatron/GAIL_PPO

Generative Adversarial Imitation Learning

https://github.com/morikatron/PPO

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/morning9393/HAPPO-HATRPO

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

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