Papers with Code 아카이브

Reinforcement Learning (RL)

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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

코드

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

https://github.com/wwydmanski/rl_tennis

Continuous control with deep reinforcement learning

https://github.com/wxj77/TransferReinforcementLearning

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/wyattlansford/MineRL

MOReL : Model-Based Offline Reinforcement Learning

https://github.com/wyndwarrior/imitation_from_observation

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/wyz2368/open_spiel2

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/wyz2368/open_spiel3

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/wyz2368/open_spiel_egta

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/xValentim/Steering_Behaviors_with_pygame

Playing Atari with Deep Reinforcement Learning

https://github.com/xbpeng/DeepMimic

DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills

https://github.com/xbpeng/awr

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

https://github.com/xgfelicia/Reinforcement-Learning

Deep Reinforcement Learning with Double Q-learning

https://github.com/xgfelicia/SRVRPG

Sample Efficient Policy Gradient Methods with Recursive Variance Reduction

https://github.com/xiangyu-liu/Computer-Network

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

https://github.com/xiaobai714/image_caption

Self-critical Sequence Training for Image Captioning

https://github.com/xiaojunxu/SQLNet

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

https://github.com/xiaomi-automl/FALSR

Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search

https://github.com/xiexiexiaoxiexie/Udacity-self-driving-car-engineer-P7-Highway-Driving

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

https://github.com/xingyu-lin/softagent

Learning to Manipulate Deformable Objects without Demonstrations

https://github.com/xinjinghao/sparrow-v1

Prioritized Experience Replay

https://github.com/xionghuichen/maple

NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning

https://github.com/xiuyu0000/new_papers_codes/tree/main/MAML

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

https://github.com/xiuyu0000/new_papers_codes/tree/main/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/xiuyu0000/new_papers_codes/tree/main/mappo

The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

https://github.com/xiuyu0000/new_papers_codes/tree/main/qmix

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

https://github.com/xiuyu0000/new_papers_codes/tree/main/sac

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/xkotaro/lockdowneR

Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning

https://github.com/xlrshop/Parl

ParlAI: A Dialog Research Software Platform

https://github.com/xtma/simple-pytorch-rl

Continuous control with deep reinforcement learning

https://github.com/xuehy/pytorch-maddpg

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

https://github.com/xuewyang/Fashion-Image-Captioning-Benchmark

Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards

https://github.com/xuezeyue/dancegrpo

DanceGRPO: Unleashing GRPO on Visual Generation

https://github.com/xujinfan/Reinforced-Continual-Learning

Reinforced Continual Learning

https://github.com/xukaidi13/Simulation-code

Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet

https://github.com/xumengyaamy/swinmlp_trancap

Surgical Instruction Generation with Transformers

https://github.com/xusophia/DataSciFinalProj

Prioritized Experience Replay

https://github.com/xuyuandong/simple-ddpg

Continuous control with deep reinforcement learning

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