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

https://github.com/montaserFath/Reinforcement-Learning-for-Prosthetics

Proximal Policy Optimization Algorithms

https://github.com/monte-ai/expert-augmented-acktr

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/morikatron/GAIL_PPO

Proximal Policy Optimization Algorithms

https://github.com/morikatron/PPO

Proximal Policy Optimization Algorithms

https://github.com/morning9393/HAPPO-HATRPO

Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

https://github.com/mrahtz/learning-from-human-preferences

Deep Exploration via Bootstrapped DQN

https://github.com/mu-y/lll-tts

Overcoming catastrophic forgetting in neural networks

https://github.com/muupan/async-rl

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/nancyhwr/DQN_Ram

Learning from the memory of Atari 2600

https://github.com/nandomp/AICollaboratory

The Arcade Learning Environment: An Evaluation Platform for General Agents

https://github.com/nathangrinsztajn/Box-World

Relational Deep Reinforcement Learning

https://github.com/nathanin/pad

Playing Atari with Deep Reinforcement Learning

https://github.com/natsumeS/analysis

Playing Atari with Deep Reinforcement Learning

https://github.com/nbopardi/smb

Deep Reinforcement Learning with Double Q-learning

https://github.com/near32/regym

Playing Atari with Deep Reinforcement Learning

https://github.com/neilsgp/RL-Algorithms

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/neka-nat/distributed_rl

Distributed Prioritized Experience Replay

https://github.com/neuro-soup/es-torch

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/ngoodger/nle-language-wrapper

Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

https://github.com/nhynes/abc

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

https://github.com/nice-hku/cl2marl-smac

Counterfactual Multi-Agent Policy Gradients

https://github.com/nicoladainese96/RelationalDeepRL

Relational Deep Reinforcement Learning

https://github.com/nicoladainese96/SC2-RL

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/nikaashpuri/sarfa-saliency

Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution

https://github.com/nikhilbarhate99/PPO

Proximal Policy Optimization Algorithms

https://github.com/nikhilbarhate99/PPO-PyTorch

Proximal Policy Optimization Algorithms

https://github.com/nikhilbarhate99/min-decision-transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/niklasschmitz/DeepQLearning

Playing Atari with Deep Reinforcement Learning

https://github.com/ninja18/AtariDQN

Playing Atari with Deep Reinforcement Learning

https://github.com/nishantgurunath/HRL

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

https://github.com/nitthilan/pommerman

Trust Region Policy Optimization

https://github.com/nju-rl/acorm

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

https://github.com/nnaisense/pgpelib

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/nolanwinsman/Team-Doom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/nolisten/erl

Count-Based Exploration with Neural Density Models

https://github.com/nric/ProximalPolicyOptimizationContinuousKeras

Proximal Policy Optimization Algorithms

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