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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/open-dynamic-robot-initiative/trifinger_simulation

TriFinger: An Open-Source Robot for Learning Dexterity

https://github.com/openai/maddpg

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

https://github.com/openai/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/openai/universe-starter-agent

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/openai/weightnorm

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

https://github.com/opendilab/DI-engine

Deep Reinforcement Learning with Double Q-learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/acer.py

Sample Efficient Actor-Critic with Experience Replay

https://github.com/opendilab/DI-engine/blob/main/ding/policy/pdqn.py

Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces

https://github.com/opendilab/DI-engine/blob/main/ding/policy/rainbow.py

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/sac.py

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

https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/rnd_reward_model.py

Exploration by Random Network Distillation

https://github.com/openrlbenchmark/openrlbenchmark

Deep Reinforcement Learning at the Edge of the Statistical Precipice

https://github.com/opent03/neural_cars

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

https://github.com/opplieam/Pong-Deep-RL

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/optimization-toolbox/DNE4py

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

https://github.com/optimization-toolbox/deep-neuroevolution-mpi4py

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

https://github.com/otoofim/ObjLocalisation

Active Object Localization with Deep Reinforcement Learning

https://github.com/oxquantum-repo/drl_for_quantum_measurement

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

https://github.com/p-morais/gym-cassie

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

https://github.com/paige-chang/Music-Recommendation-System

Deep Reinforcement Learning for List-wise Recommendations

https://github.com/paintception/Deep-Quality-Value-DQV-Learning-

Deep Quality-Value (DQV) Learning

https://github.com/paintception/Deep-Quality-Value-Family

Playing Atari with Deep Reinforcement Learning

https://github.com/pairlab/robosuite

Reinforcement and Imitation Learning for Diverse Visuomotor Skills

https://github.com/parilo/rl-server

Playing Atari with Deep Reinforcement Learning

https://github.com/parthjaggi/flow

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/pathway/alphaxos

Deep Reinforcement Learning with Double Q-learning

https://github.com/pavitrakumar78/Playing-custom-games-using-Deep-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/pengyuan-zhou/Multi-agent-RL-traffic-light-control

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/pereirarodrigo/project_spike

Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games

https://github.com/pereirarodrigo/spike

Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games

https://github.com/peterhj/arm-pytorch

Regret Minimization for Partially Observable Deep Reinforcement Learning

https://github.com/petsol/ContinuousControl_UnityAgent_DDPG_Udacity

Continuous control with deep reinforcement learning

https://github.com/petsol/MultiAgentCooperation_UnityAgent_MADDPG_Udacity

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

https://github.com/philtabor/Deep-Q-Learning-Paper-To-Code

Deep Reinforcement Learning with Double Q-learning

https://github.com/philtabor/Multi-Agent-Deep-Deterministic-Policy-Gradients

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

https://github.com/phixion/phixion

Reinforcement Learning: An Overview

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