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

https://github.com/idsia/neuraldiffeq-fwp

Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

https://github.com/ifestus/rl

Trust Region Policy Optimization

https://github.com/ifpen/wfcrl-benchmark

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

https://github.com/iglu-contest/iglu-builder-baseline-dreamer

Mastering Atari with Discrete World Models

https://github.com/igoracmorais/inteligencia_artificial

Playing Atari with Deep Reinforcement Learning

https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail

Proximal Policy Optimization Algorithms

https://github.com/ikostrikov/pytorch-a3c

Asynchronous Methods for Deep Reinforcement Learning

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

Trust Region Policy Optimization

https://github.com/ikostrikov/pytorch-trpo

Trust Region Policy Optimization

https://github.com/inoryy/reaver

Proximal Policy Optimization Algorithms

https://github.com/instadeepai/flashbax

Prioritized Experience Replay

https://github.com/intellisys-lab/stellaris-sc24

Proximal Policy Optimization Algorithms

https://github.com/invictos/InsacarDQN

Playing Atari with Deep Reinforcement Learning

https://github.com/isp1tze/MAProj

Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

https://github.com/jachiam/cpo

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/jackal-u/CSGOAPI

Counter-Strike Deathmatch with Large-Scale Behavioural Cloning

https://github.com/jacobkooi/hadamax

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/jadag/DDQN_mario

Deep Reinforcement Learning with Double Q-learning

https://github.com/jakegrigsby/supersonic

Exploration by Random Network Distillation

https://github.com/janhuenermann/neurojs

Increasing the Action Gap: New Operators for Reinforcement Learning

https://github.com/jcoreyes/evolvingrl

Evolving Reinforcement Learning Algorithms

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

Proximal Policy Optimization Algorithms

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

Proximal Policy Optimization Algorithms

https://github.com/jeapostrophe/monaco

Monte Carlo Tree Search for Asymmetric Trees

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/jerrodparker20/adaptive-transformers-in-rl

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

https://github.com/jezzarax/drlnd_p1_navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/jfpettit/flare

Proximal Policy Optimization Algorithms

https://github.com/jhare96/reinforcement-learning

Proximal Policy Optimization Algorithms

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

Deep Reinforcement Learning with Double Q-learning

https://github.com/jjgarau/DAGPolicyGradient

Evolving Reinforcement Learning Algorithms

https://github.com/jk96491/C-COMA

The StarCraft Multi-Agent Challenge

https://github.com/jk96491/SMAC

The StarCraft Multi-Agent Challenge

https://github.com/johan-gras/MuZero

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/johannah/bootstrap_dqn

Deep Exploration via Bootstrapped DQN

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