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Deep Reinforcement Learning

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코드

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

https://github.com/jakegrigsby/deep_control/blob/master/deep_control/ddpg.py

Continuous control with deep reinforcement learning

https://github.com/jakegrigsby/deep_control/blob/master/deep_control/grac.py

GRAC: Self-Guided and Self-Regularized Actor-Critic

https://github.com/jakegrigsby/deep_control/blob/master/deep_control/sac.py

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

https://github.com/jakegrigsby/supersonic

Exploration by Random Network Distillation

https://github.com/jakub-zarzycki2022/gym-pbn-stac

Deep Reinforcement Learning for Control of Probabilistic Boolean Networks

https://github.com/jansenkeith501/CS295-MADDPG

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

https://github.com/jcoreyes/ex2

EX2: Exploration with Exemplar Models for Deep Reinforcement Learning

https://github.com/jcwleo/curiosity-driven-exploration-pytorch

Efficient Parallel Methods for Deep Reinforcement Learning

https://github.com/jcwleo/random-network-distillation-pytorch

Efficient Parallel Methods for Deep Reinforcement Learning

https://github.com/jeffchy/Artificial-Idiot

Playing FPS Games with Deep Reinforcement Learning

https://github.com/jeffery1236/Atari_DoubleDeepQNetwork

Deep Reinforcement Learning with Double Q-learning

https://github.com/jelleman8/TractSeg

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

https://github.com/jezzarax/drlnd_p1_navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/jfpettit/jfpettit.github.io

Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning

https://github.com/jhu-lcsr/good_robot

Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning

https://github.com/jiayu-ch15/MPE-for-curriculum-learning

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

https://github.com/jihoonerd/Deep-Reinforcement-Learning-with-Double-Q-learning

Deep Reinforcement Learning with Double Q-learning

https://github.com/jincan333/lot

CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms

https://github.com/jingdic/rgmcomm

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

https://github.com/jingwu6/handful-of-trials-in-pytorch

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

https://github.com/jj-zhu/resource_aware_control_rl

Deep Reinforcement Learning for Event-Triggered Control

https://github.com/jjenster/Test

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/jmribeiro/UGP

Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement Learning

https://github.com/jmvines20/ZhengyaoJiang-PGPortfolio

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/joanfmendo/Deep-Reinforcement-Learning-Algorithm-for-Dynamic-Pricing-of-Express-Lanes-with-Multiple-Access-Loca

Deep Reinforcement Learning Algorithm for Dynamic Pricing of Express Lanes with Multiple Access Locations

https://github.com/johannah/bootstrap_dqn

Randomized Prior Functions for Deep Reinforcement Learning

https://github.com/johannesharmse/multi_agent_RL

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

https://github.com/jonasrothfuss/promp

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

https://github.com/jonaths/dqn-grid

Playing Atari with Deep Reinforcement Learning

https://github.com/jonaths/tf-dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/joshiatul/game_playing

Playing Atari with Deep Reinforcement Learning

https://github.com/jqueeney/geppo

Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse

https://github.com/jrobine/actor-critic

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

https://github.com/jsanderink/tue

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

https://github.com/jsztompka/DuelDQN

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/junmokane/offline-PEARL

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

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