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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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

https://github.com/DevSlem/AINE-DRL

Proximal Policy Optimization Algorithms

https://github.com/Dorozhko-Anton/go-explore

Go-Explore: a New Approach for Hard-Exploration Problems

https://github.com/DuaneNielsen/rnd

Exploration by Random Network Distillation

https://github.com/EconomistGrant/HTFE-tensortrade

Proximal Policy Optimization Algorithms

https://github.com/EmptyJackson/groove

Discovering Reinforcement Learning Algorithms

https://github.com/Epsilon10/MARL

Soft Actor-Critic for Discrete Action Settings

https://github.com/FMArduini/python-rl

Proximal Policy Optimization Algorithms

https://github.com/FaboNo/DRLND

Deep Reinforcement Learning with Double Q-learning

https://github.com/FauzaanQureshi/deep-Q-learning

Playing Atari with Deep Reinforcement Learning

https://github.com/FlixCoder/rust-es-optimizer

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/FragileTech/FractalAI

Fractal AI: A fragile theory of intelligence

https://github.com/GabrielAlacchi/atari-ari-extensions

Unsupervised State Representation Learning in Atari

https://github.com/GaoFangshu/ELF-example

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

https://github.com/Gary-Shi/Tank

Playing Atari with Deep Reinforcement Learning

https://github.com/GiadaSimionato/Reasoning_Agents_2020

Proximal Policy Optimization Algorithms

https://github.com/GoingMyWay/dopamine_reward_decomposition

Prioritized Experience Replay

https://github.com/Gouet/Acrobot-PPO

Proximal Policy Optimization Algorithms

https://github.com/Gouet/Breakout-V0

Proximal Policy Optimization Algorithms

https://github.com/Gouet/PPO-gym

Proximal Policy Optimization Algorithms

https://github.com/Gouet/PPO-pytorch

Proximal Policy Optimization Algorithms

https://github.com/Gregory-Eales/proximal-policy-optimization

Proximal Policy Optimization Algorithms

https://github.com/GuanSuns/Simple-Human-in-the-Loop-ML-Interface

Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation

https://github.com/Guillaume-Cr/lunar_lander_per

Prioritized Experience Replay

https://github.com/Guillemdb/FractalAI

Fractal AI: A fragile theory of intelligence

https://github.com/Guillemdb/fragile

Fractal AI: A fragile theory of intelligence

https://github.com/HanbumKo/distributed-wqmix

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

https://github.com/Howuhh/prioritized_experience_replay

Prioritized Experience Replay

https://github.com/HussonnoisMaxence/RL_Algorithms

Deep Reinforcement Learning with Double Q-learning

https://github.com/HzcIrving/DecisionTransformer_StepbyStep

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/IDSIA/lmtool-fwms

Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

https://github.com/IDSIA/lmtool-fwp

Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

https://github.com/InSpaceAI/RL-Zoo

Playing Atari with Deep Reinforcement Learning

https://github.com/Ipsedo/EvoMotion

Proximal Policy Optimization Algorithms

https://github.com/Ishan-Kumar2/Reinforcement-Learning-on-2048

Playing Atari with Deep Reinforcement Learning

https://github.com/Itomigna2/Muesli-lunarlander

Muesli: Combining Improvements in Policy Optimization

https://github.com/JBGUIMBAUD/deep-reenforcement-learning

Dueling Network Architectures for Deep Reinforcement Learning

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