https://github.com/chainer/chainerrl
Rainbow: Combining Improvements in Deep Reinforcement Learning
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Montezuma's Revenge
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Montezuma's Revenge is an ATARI 2600 Benchmark game that is known to be difficult to perform on for reinforcement learning algorithms. Solutions typically employ algorithms that incentivise environment exploration in different ways.
For the state-of-the art tables, please consult the parent Atari Games task.
<span style="color:grey; opacity: 0.6">( Image credit: Q-map )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/chandar-lab/RLHive
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/coreylowman/rl_simply
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/daviddcho/supermario
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/deconlabs/Binanace-trading-simulation
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/deconlabs/Binanace_trading_simulation
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/deconlabs/TradingZoo-Dynamic-fee-simulation
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/deniztekalp/comp-491-bitirme
Beating Atari with Natural Language Guided Reinforcement Learning
https://github.com/dsapandora/s_cera
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/eddynelson/dqn
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/efg59/Rainbow
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/fabiopardo/qmap
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks
https://github.com/facebookresearch/Horizon
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/facebookresearch/ReAgent
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/facebookresearch/minihack
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs
https://github.com/floringogianu/atari-agents
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/forevergj/multithreading_ppo_RND
https://github.com/htdt/rainbow
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/jacobkooi/hadamax
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/jakegrigsby/supersonic
https://github.com/jcwleo/random-network-distillation-pytorch
https://github.com/jhare96/reinforcement-learning
https://github.com/jvoynow/DQN-analysis-with-2048
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/kngwyu/intrinsic-rewards
https://github.com/learningandplanningICLR/learningandplanning
https://github.com/lgerrets/rl18-curiosity
https://github.com/lireer/ricochet-robot-solver
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/liuyuezhang/pyrl
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/lyebi/Test
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
https://github.com/madeleinesnyder/Subbat
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
https://github.com/michaelnny/deep_rl_zoo
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/michalnand/reinforcement_learning
https://github.com/microsoft/strategically_efficient_rl
https://github.com/mindspore-courses/Deep-Reinforcement-Learning-Algorithms-with-MindSpore
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
https://github.com/mindspore-courses/Rainbow-MindSpore
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/mingyip/Rainbow-Reinforcement-Learning-For-Chess-Games
Rainbow: Combining Improvements in Deep Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.