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

https://github.com/devindeng94/llm-smac

공식 코드

A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language Models

https://github.com/devindeng94/smac-hard

공식 코드

SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMAC

https://github.com/dig-beihang/ami

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Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence

https://github.com/dixantmittal/expose

공식 코드

ExPoSe: Combining State-Based Exploration with Gradient-Based Online Search

https://github.com/dojeon-ai/atari-pb

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Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning

https://github.com/dsbrown1331/bayesianrex

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Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences

https://github.com/duyunshu/lucid-dreaming-for-exp-replay

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Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy

https://github.com/ercumentilhan/advice-imitation-reuse

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Learning on a Budget via Teacher Imitation

https://github.com/ercumentilhan/advice-novelty

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Student-Initiated Action Advising via Advice Novelty

https://github.com/ercumentilhan/naive-advice-imitation

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Action Advising with Advice Imitation in Deep Reinforcement Learning

https://github.com/facebook/MazeBase

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MazeBase: A Sandbox for Learning from Games

https://github.com/facebookresearch/CollaQ

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Multi-Agent Collaboration via Reward Attribution Decomposition

https://github.com/facebookresearch/ELF

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

https://github.com/facebookresearch/diplomacy_searchbot

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No-Press Diplomacy from Scratch

https://github.com/facebookresearch/e3b

공식 코드

A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs

https://github.com/facebookresearch/nle

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Dungeons and Data: A Large-Scale NetHack Dataset

https://github.com/facebookresearch/starcraft_defogger

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Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger

https://github.com/farama-foundation/arcade-learning-environment

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CALE: Continuous Arcade Learning Environment

https://github.com/flairox/jaxmarl

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JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

https://github.com/fmorenovr/DRDQ-Atari

공식 코드

Performing Deep Recurrent Double Q-Learning for Atari Games

https://github.com/gabrieledcjr/DeepRL

공식 코드

Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning

https://github.com/giuse/DNE

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Playing Atari with Six Neurons

https://github.com/glample/Arnold

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Playing FPS Games with Deep Reinforcement Learning

https://github.com/gomiss/action-balance-exploration

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Exploring Unknown States with Action Balance

https://github.com/google-deepmind/sketch_dqn

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Distributional Bellman Operators over Mean Embeddings

https://github.com/google-research/batch_rl

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An Optimistic Perspective on Offline Reinforcement Learning

https://github.com/google-research/google-research

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Learning Abstract Models for Strategic Exploration and Fast Reward Transfer

https://github.com/google-research/google-research/tree/master/generalization_representations_rl_aistats22

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On the Generalization of Representations in Reinforcement Learning

https://github.com/google-research/google-research/tree/master/memento

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On Catastrophic Interference in Atari 2600 Games

https://github.com/google-research/google-research/tree/master/mico

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MICo: Improved representations via sampling-based state similarity for Markov decision processes

https://github.com/google-research/google-research/tree/master/multi_game_dt

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Multi-Game Decision Transformers

https://github.com/google-research/google-research/tree/master/munchausen_rl

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Munchausen Reinforcement Learning

https://github.com/google-research/reincarnating_rl

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Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress

https://github.com/google/brain_autorl

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Evolving Reinforcement Learning Algorithms

https://github.com/google/brain_autorl/tree/main/evolving_rl

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Evolving Reinforcement Learning Algorithms

https://github.com/grockious/deepsynth

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DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning

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