Papers with Code 아카이브

Reinforcement Learning

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코드

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

https://github.com/sseol11/ParlAI_blender_persona

ParlAI: A Dialog Research Software Platform

https://github.com/ssg-research/dawn-dynamic-adversarial-watermarking-of-neural-networks

Knockoff Nets: Stealing Functionality of Black-Box Models

https://github.com/ssokota/mmd

A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games

https://github.com/stanford-iris-lab/batch-exploration

Batch Exploration with Examples for Scalable Robotic Reinforcement Learning

https://github.com/stanfordnlp/cocoa

Decoupling Strategy and Generation in Negotiation Dialogues

https://github.com/starry-sky6688/MADDPG

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

https://github.com/starry-sky6688/marl-algorithms

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/stat-ml/dpp-dropout-uncertainty

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/statsu1990/regression_dropout

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/stavrosgreece/MultiAgentLearning

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/stefanowoerner/medimeta-pytorch

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

https://github.com/stephanboner/gabor_data

Recurrent Models of Visual Attention

https://github.com/stephen-chung-mh/map_prop

MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents

https://github.com/stevenpjg/RDPG

Memory-based control with recurrent neural networks

https://github.com/stevenpjg/ddpg-aigym

Continuous control with deep reinforcement learning

https://github.com/steventango/jumpstart-rl

Jump-Start Reinforcement Learning

https://github.com/stevep2007/https-github.com-ZhengyaoJiang-PGPortfolio

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/stevezhang1990/dma_rl

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

https://github.com/stewartyoung/DeepRL-PM

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/stratismarkou/sample-efficient-bayesian-rl

Posterior Sampling for Reinforcement Learning Without Episodes

https://github.com/subhadip-maiti/tinydqn

Playing Atari with Deep Reinforcement Learning

https://github.com/subinlab/model_based_rl_paper

Temporal Difference Variational Auto-Encoder

https://github.com/sudh1976/ECE510_DeepLearning_Project

An Alternative Softmax Operator for Reinforcement Learning

https://github.com/sufengniu/GVIN

Value Iteration Networks

https://github.com/suhoy901/SeqGAN

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/suhruthGit/Unity3DML

Unity: A General Platform for Intelligent Agents

https://github.com/sujoung/debuggedDDQ

Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

https://github.com/sukiboo/personalization_wain21

On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks

https://github.com/sulaimanalmani/k8s_srsran_open5gs

Generalizable Resource Scaling of 5G Slices using Constrained Reinforcement Learning

https://github.com/sumedh7/RoboCLIP

RoboCLIP: One Demonstration is Enough to Learn Robot Policies

https://github.com/sumitsk/HER

Hindsight Experience Replay

https://github.com/sumitsk/marl_transfer

Learning Transferable Cooperative Behavior in Multi-Agent Teams

https://github.com/sumkumar/hiro_impl

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/sunfex/weighted-sac

Prioritized Experience Replay

https://github.com/sunjeet95/Deep-Q-Network-using-Tensorflow

Playing Atari with Deep Reinforcement Learning

https://github.com/sunnyyzj/morl-baselines

A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation

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