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

https://github.com/aman-khurana/deep-q-learning

Deep Reinforcement Learning with Double Q-learning

https://github.com/amanda-lambda/drl-experiments

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/amanda-lambda/hack-flappy-bird-drl

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/amartyamukherjee/ppo-packcooling

Proximal Policy Optimization Algorithms

https://github.com/amaudruz/RL_openaigym

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/ameet-1997/Prioritized_Experience_Replay

Prioritized Experience Replay

https://github.com/amidos2006/Mario-AI-Framework

Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network

https://github.com/amidos2006/etpkldiv

Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network

https://github.com/amirmirzaei79/CartPole-DQN-And-DDQN

Deep Reinforcement Learning with Double Q-learning

https://github.com/amzoyang/CS-221-Final-Project

Proximal Policy Optimization Algorithms

https://github.com/andrejorsula/drl_grasping

Mastering Atari with Discrete World Models

https://github.com/andris955/diplomaterv

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

https://github.com/andyljones/cule

Accelerating Reinforcement Learning through GPU Atari Emulation

https://github.com/andyljones/zonotable

Proximal Policy Optimization Algorithms

https://github.com/angelolovatto/raylab

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

https://github.com/anh-nn01/Lunar-Lander-Double-Deep-Q-Networks

Deep Reinforcement Learning with Double Q-learning

https://github.com/anhtu293/NeurIPS-2019-Challenge

Prioritized Experience Replay

https://github.com/anirudh9119/RIMs

Recurrent Independent Mechanisms

https://github.com/anita-hu/TF2-RL

Playing Atari with Deep Reinforcement Learning

https://github.com/ankeshanand/atari-representation-learning

Unsupervised State Representation Learning in Atari

https://github.com/anonymous-iclr22/trust-region-in-multi-agent-reinforcement-learning

Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

https://github.com/anonymous1234517/code

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/anthonysong98/super-mario-bros-ppo

Proximal Policy Optimization Algorithms

https://github.com/apollopower/DOOM-AI

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/apsdehal/gym-starcraft

Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

https://github.com/araffin/sbx

Proximal Policy Optimization Algorithms

https://github.com/aravindsrinivas/curl_rainbow

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

https://github.com/ariseff/overcoming-catastrophic

Overcoming catastrophic forgetting in neural networks

https://github.com/arnomoonens/yarll

Trust Region Policy Optimization

https://github.com/ars-ashuha/quantile-regression-dqn-pytorch

Distributional Reinforcement Learning with Quantile Regression

https://github.com/asjad99/Reinforcement-Learning

Reward learning from human preferences and demonstrations in Atari

https://github.com/asmerdov/DataCollectionSystem

Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

https://github.com/asparius/barlowrl

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

https://github.com/aspk/space_battle

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/astooke/accel_rl

Accelerated Methods for Deep Reinforcement Learning

https://github.com/astooke/rlpyt

Accelerated Methods for Deep Reinforcement Learning

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