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

https://github.com/starry-sky6688/marl-algorithms

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/stavrosgreece/MultiAgentLearning

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/stijani/elastic-weight-consolidation-tf2

Overcoming catastrophic forgetting in neural networks

https://github.com/subhadip-maiti/tinydqn

Playing Atari with Deep Reinforcement Learning

https://github.com/sunfex/weighted-sac

Prioritized Experience Replay

https://github.com/sunjeet95/Deep-Q-Network-using-Tensorflow

Playing Atari with Deep Reinforcement Learning

https://github.com/sunjing1102628/DOMAC

Decision-making with Speculative Opponent Models

https://github.com/susanwe/ope_worldmodels

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/suyoung-lee/Episodic-Backward-Update

Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening

https://github.com/sygi/deep_q_rl

Playing Atari with Deep Reinforcement Learning

https://github.com/synthlabsai/big-math

Proximal Policy Optimization Algorithms

https://github.com/szrlee/GPT-HyperAgent

Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent

https://github.com/szrlee/ensemble_plus_plus

Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent

https://github.com/taku-y/20181125-pybullet

Proximal Policy Optimization Algorithms

https://github.com/takuseno/d3rlpy

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/takuseno/ppo

Proximal Policy Optimization Algorithms

https://github.com/tatsu-lab/linguistic_calibration

Proximal Policy Optimization Algorithms

https://github.com/tcmxx/CNTKUnityTools

Playing Atari with Deep Reinforcement Learning

https://github.com/tencent-ailab/Arena

Arena: a toolkit for Multi-Agent Reinforcement Learning

https://github.com/tensorflow/models

Deep Exploration via Bootstrapped DQN

https://github.com/tensorflow/models/tree/master/research/deep_contextual_bandits

Deep Exploration via Bootstrapped DQN

https://github.com/tensorlayer/RLzoo

Trust Region Policy Optimization

https://github.com/tensorpack/tensorpack/tree/master/examples/A3C-Gym

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/tensorpack/tensorpack/tree/master/examples/DeepQNetwork

Playing Atari with Deep Reinforcement Learning

https://github.com/theSparta/neural-symbolic-machines

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/theresearchai/vehicle_routing_rl_2

Proximal Policy Optimization Algorithms

https://github.com/thomas-schillaci/SimPLe

Model-Based Reinforcement Learning for Atari

https://github.com/thomashirtz/noisy-networks

Noisy Networks for Exploration

https://github.com/threewisemonkeys-as/torched_impala

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/thu-ml/tianshou

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/tidedra/vl-rlhf

Proximal Policy Optimization Algorithms

https://github.com/tilkb/thermoai

Proximal Policy Optimization Algorithms

https://github.com/tjuhaoxiaotian/pymarl3

The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

https://github.com/tkcoding/DeepRL

Deep Reinforcement Learning with Double Q-learning

https://github.com/tkcoding/Stock_DRL

Deep Reinforcement Learning with Double Q-learning

https://github.com/tlikhomanenko/optimize-coordconv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

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