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

https://github.com/gsastry/human-rl

공식 코드

Trial without Error: Towards Safe Reinforcement Learning via Human Intervention

https://github.com/guillermopuebla/rrl

공식 코드

Learning Relational Rules from Rewards

https://github.com/hadrien-pouget/Ranking-Policy-Decisions

공식 코드

Ranking Policy Decisions

https://github.com/hahayonghuming/VDACs

공식 코드

Value-Decomposition Multi-Agent Actor-Critics

https://github.com/hanhanAnderson/LSF-SAC

공식 코드

Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients

https://github.com/hanhananderson/pac-marl

공식 코드

PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning

https://github.com/harfang3d/dogfight-sandbox-hg2

공식 코드

Harfang3D Dog-Fight Sandbox: A Reinforcement Learning Research Platform for the Customized Control Tasks of Fighter Aircrafts

https://github.com/hellochick/FICM

공식 코드

Exploration via Flow-Based Intrinsic Rewards

https://github.com/hijkzzz/pymarl2

공식 코드

Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning

https://github.com/histmeisah/large-language-models-play-starcraftii

공식 코드

Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach

https://github.com/hsvgbkhgbv/shapley-q-learning

공식 코드

SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning

https://github.com/htdt/tempal

공식 코드

Temporal Alignment for History Representation in Reinforcement Learning

https://github.com/hyeon1996/fox

공식 코드

FoX: Formation-aware exploration in multi-agent reinforcement learning

https://github.com/hyunghona/emu

공식 코드

Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning

https://github.com/ibm/atari-active-learning

공식 코드

Is Policy Learning Overrated?: Width-Based Planning and Active Learning for Atari

https://github.com/improbable-ai/eipo

공식 코드

Redeeming Intrinsic Rewards via Constrained Optimization

https://github.com/inspirai/wilderness-scavenger

공식 코드

WILD-SCAV: Benchmarking FPS Gaming AI on Unity3D-based Environments

https://github.com/intelligent-control-lab/absolute-policy-optimization

공식 코드

Absolute Policy Optimization

https://github.com/iskandor/snd

공식 코드

Self-supervised network distillation: an effective approach to exploration in sparse reward environments

https://github.com/j3soon/dfac

공식 코드

DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning

https://github.com/j3soon/dfac-extended

공식 코드

A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning

https://github.com/jeanharb/a2oc_delib

공식 코드

When Waiting is not an Option : Learning Options with a Deliberation Cost

https://github.com/jessefarebro/dqn-ale

공식 코드

Generalization and Regularization in DQN

https://github.com/jiwonjeon9603/maser

공식 코드

MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer

https://github.com/jjbong/strangeness_exploration

공식 코드

Strangeness-driven Exploration in Multi-Agent Reinforcement Learning

https://github.com/jramapuram/variational_saccading

공식 코드

Variational Saccading: Efficient Inference for Large Resolution Images

https://github.com/junaiddk/transmix

공식 코드

Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraft

https://github.com/junhyukoh/self-imitation-learning

공식 코드

Self-Imitation Learning

https://github.com/junhyukoh/value-prediction-network

공식 코드

Value Prediction Network

https://github.com/justkim/two-player-atari-rl

공식 코드

Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 Games

https://github.com/jzitovsky/sbv

공식 코드

Revisiting Bellman Errors for Offline Model Selection

https://github.com/k4ntz/activation-functions

공식 코드

Adaptive Rational Activations to Boost Deep Reinforcement Learning

https://github.com/k4ntz/moc

공식 코드

Boosting Object Representation Learning via Motion and Object Continuity

https://github.com/k4ntz/oc_atari

공식 코드

OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments

https://github.com/kaszanas/sc2_datasets

공식 코드

SC2EGSet: StarCraft II Esport Replay and Game-state Dataset

https://github.com/kazizzad/BDQN-MxNet-Gluon

공식 코드

Efficient Exploration through Bayesian Deep Q-Networks

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