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

https://github.com/michalop/startrain

공식 코드

Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft II

https://github.com/miguelsvasco/muse

공식 코드

How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents

https://github.com/mila-iqia/SGI

공식 코드

Pretraining Representations for Data-Efficient Reinforcement Learning

https://github.com/mila-iqia/atari-representation-learning

공식 코드

Unsupervised State Representation Learning in Atari

https://github.com/ml-jku/baselines-rudder

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RUDDER: Return Decomposition for Delayed Rewards

https://github.com/ml-jku/helm

공식 코드

Semantic HELM: A Human-Readable Memory for Reinforcement Learning

https://github.com/ml-jku/rudder

공식 코드

RUDDER: Return Decomposition for Delayed Rewards

https://github.com/ml-research/rational_activations

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Adaptive Rational Activations to Boost Deep Reinforcement Learning

https://github.com/ml-research/rational_rl

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Adaptive Rational Activations to Boost Deep Reinforcement Learning

https://github.com/ml-research/rational_sl

공식 코드

Adaptive Rational Activations to Boost Deep Reinforcement Learning

https://github.com/modanesh/Differential_IG

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Re-understanding Finite-State Representations of Recurrent Policy Networks

https://github.com/mohmdelsayed/streaming-drl

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Streaming Deep Reinforcement Learning Finally Works

https://github.com/morning9393/optimal-baseline-for-multi-agent-policy-gradients

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Settling the Variance of Multi-Agent Policy Gradients

https://github.com/mttga/pymarl_transformers

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TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems

https://github.com/mwydmuch/ViZDoom

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

https://github.com/mzho7212/LICA

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Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning

https://github.com/nadavbh12/Retro-Learning-Environment

공식 코드

Playing SNES in the Retro Learning Environment

https://github.com/nanxintin/StarCraft-AI

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StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning

https://github.com/navid-naderi/GraphMIX

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Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning

https://github.com/navneet-nmk/pytorch-rl

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Empowerment-driven Exploration using Mutual Information Estimation

https://github.com/neurips2019submission/Formal-Language-Constraints-for-Markov-Decision-Processes

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Formal Language Constraints for Markov Decision Processes

https://github.com/nikonikolov/rltf

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Information-Directed Exploration for Deep Reinforcement Learning

https://github.com/nkai-decision-team/llm-pysc2

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LLM-PySC2: Starcraft II learning environment for Large Language Models

https://github.com/nrasajski/behave

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BehAVE: Behaviour Alignment of Video Game Encodings

https://github.com/nudt-bi-marl/pmat

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PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning

https://github.com/nvlabs/bcts

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Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction

https://github.com/openai/baselines

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

https://github.com/openai/evolution-strategies-starter

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Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/openai/large-scale-curiosity

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Large-Scale Study of Curiosity-Driven Learning

https://github.com/openai/random-network-distillation

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Exploration by Random Network Distillation

https://github.com/opendilab/ace

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ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency

https://github.com/oxwhirl/pymarl

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

https://github.com/oxwhirl/smac

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The StarCraft Multi-Agent Challenge

https://github.com/oxwhirl/smacv2

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SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

https://github.com/oxwhirl/treeqn

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TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning

https://github.com/oxwhirl/wqmix

공식 코드

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

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