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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/beauCoker/bayesian_neural_networks

Weight Uncertainty in Neural Networks

https://github.com/behzaad/Deep_QLearning

Playing Atari with Deep Reinforcement Learning

https://github.com/behzadanksu/rlattack-dev

Noisy Networks for Exploration

https://github.com/belepi93/Ape-X

Distributed Prioritized Experience Replay

https://github.com/benedekrozemberczki/benedekrozemberczki

The Shapley Value in Machine Learning

https://github.com/bentrevett/pytorch-rl

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/berkeleyflow/flow

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/bgLeon/EMG

Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement Learning

https://github.com/bgenchel/MusicalSeqGAN

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/bgenchel/Reinforcement-Learning-for-Music-Generation

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/bharadwaj1098/Tamer

Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces

https://github.com/bhiziroglu/Language-as-an-Abstraction-for-Hierarchical-Deep-Reinforcement-Learning

Language as an Abstraction for Hierarchical Deep Reinforcement Learning

https://github.com/bhushan318/pymgridbhu

pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence Research

https://github.com/bidimensional/Tennis

Continuous control with deep reinforcement learning

https://github.com/biemann/Collaboration-and-Competition

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

https://github.com/biemann/Continuous-Control

Continuous control with deep reinforcement learning

https://github.com/bilibili/LastOrder-Dota2

Dota 2 with Large Scale Deep Reinforcement Learning

https://github.com/biorobotics/PRD_environments

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

https://github.com/bitsauce/Carla-ppo

Learning to Drive in a Day

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

Continuous control with deep reinforcement learning

https://github.com/bjotho/Zelda1AI

Playing Atari with Deep Reinforcement Learning

https://github.com/bkhmsi/meta-rl-harlow

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/blakeMilner/DeepQLearning

Playing Atari with Deep Reinforcement Learning

https://github.com/bloomsburyai/question-generation

Machine Comprehension by Text-to-Text Neural Question Generation

https://github.com/bmazoure/batch_stationary_distribution

Batch Stationary Distribution Estimation

https://github.com/bmeyers/VirtualMicrogridSegmentation

Continuous control with deep reinforcement learning

https://github.com/bo-pang/OLSPI

Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systems

https://github.com/bonniesjli/DQN_SR

Count-Based Exploration with the Successor Representation

https://github.com/bonniesjli/MADDPG_Tennis

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

https://github.com/bonniesjli/MADDPG_Tennis_UnityML

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

https://github.com/borea17/efficient_rl

Playing Atari with Deep Reinforcement Learning

https://github.com/borhanreo/Obstacle-Avoid-Car

Playing Atari with Deep Reinforcement Learning

https://github.com/bossdm/RCMDP

Robust Constrained-MDPs: Soft-Constrained Robust Policy Optimization under Model Uncertainty

https://github.com/botforge/simplementation

Deep Reinforcement Learning with Double Q-learning

https://github.com/bowenbaker/metaqnn

Designing Neural Network Architectures using Reinforcement Learning

https://github.com/bprabhakar/upside-down-reinforcement-learning

Training Agents using Upside-Down Reinforcement Learning

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