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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/MrDaubinet/collaboration-and-competition

Prioritized Experience Replay

https://github.com/MrSyee/pg-is-all-you-need

Continuous control with deep reinforcement learning

https://github.com/MuditShivendra/Traffic_Light_Control

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/N0r9st/a2c-jax

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/NOHYC/autonomous_driving_car_project

Continuous control with deep reinforcement learning

https://github.com/Narsil/rl-baselines

Exploration by Random Network Distillation

https://github.com/Nasdin/ReinforcementLearning-AtariGame

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/NeekhilD/Learning-Competition

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

https://github.com/Neo-The1/ThinkingTicTacToe

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/NervanaSystems/coach

Playing Atari with Deep Reinforcement Learning

https://github.com/Neuraxio/Neuraxle

Ray: A Distributed Framework for Emerging AI Applications

https://github.com/NikolausBerl/Udacity_DRLN_Navigation_Project

Deep Reinforcement Learning with Double Q-learning

https://github.com/Nina-Konovalova/TSP-RL-Skoltech_project

Reinforcement Learning for Solving the Vehicle Routing Problem

https://github.com/NinaPi/Lunar_Causal_Network

Explainable Reinforcement Learning Through a Causal Lens

https://github.com/NitinVishalKulkarni/OfflineReinforcementLearning

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

https://github.com/NivNayman/XNAS

DARTS: Differentiable Architecture Search

https://github.com/Nkluge-correa/teeny-tiny_castle

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/OMS1996/Carla_The_RL_Self-Driving-Car

Deep Reinforcement Learning with Double Q-learning

https://github.com/Octavio-Pappalardo/MAML_for_RL_pytorch

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

https://github.com/Octavio-Pappalardo/RL2-implementation-pytorch

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

https://github.com/Olloxan/Pytorch-A2C

Imagination-Augmented Agents for Deep Reinforcement Learning

https://github.com/OpenLLMAI/OpenLLaMA2

Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

https://github.com/OptMLGroup/VRP-RL

Reinforcement Learning for Solving the Vehicle Routing Problem

https://github.com/OptimalPandemic/taurus

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/OrthoDex/PCGrad-PyTorch

Gradient Surgery for Multi-Task Learning

https://github.com/PKU-Alignment/ProAgent

Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination

https://github.com/PKU-EPIC/PartManip

What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study

https://github.com/PaddlePaddle/PaddleClas

GridMask Data Augmentation

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/maml

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

https://github.com/PaddlePaddle/PaddleScience

Gradient Surgery for Multi-Task Learning

https://github.com/Panda0406/Adversarial-Learning-Distant-Supervision-RE

Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

https://github.com/Panda0406/Reinforcement-Learning-Distant-Supervision-RE

Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

https://github.com/Parapompadoo/Temporal_Action_Detection

A Self-Adaptive Proposal Model for Temporal Action Detection based on Reinforcement Learning

https://github.com/PaulCharnay/Projet_AIF

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/Pechckin/MountainCar

Continuous control with deep reinforcement learning

https://github.com/PeteXC/Transfer-Learning

Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

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