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

https://bitbucket.org/catk1ns0n/repr_public

공식 코드

Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting

https://github.com/0x1DA9430/Decision-Transformer-vs-Decision-Mamba

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Decision Transformer vs. Decision Mamba: Analysing the Complexity of Sequential Decision Making in Atari Games

https://github.com/29antonioac/StarCraft-winner-prediction

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RedDwarfData: a simplified dataset of StarCraft matches

https://github.com/5had3z/sc2-experiments

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Carefully Structured Compression: Efficiently Managing StarCraft II Data

https://github.com/5vision/DARQN

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Deep Attention Recurrent Q-Network

https://github.com/Acciorocketships/pymarl2

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QGNN: Value Function Factorisation with Graph Neural Networks

https://github.com/AdaCompNUS/diner-dash-simulator

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DinerDash Gym: A Benchmark for Policy Learning in High-Dimensional Action Space

https://github.com/AdrienSF/proactive-contextful-toxicity-detection

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Context-Aware Toxicity Detection in Multiplayer Games: Integrating Domain-Adaptive Pretraining and Match Metadata

https://github.com/AmazingAng/VER

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Revisiting Prioritized Experience Replay: A Value Perspective

https://github.com/AndssY/DeMa

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Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?

https://github.com/Ardavans/DSR

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Deep Successor Reinforcement Learning

https://github.com/BenedictWilkinsAI/S3N

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A Metric Learning Approach to Anomaly Detection in Video Games

https://github.com/Benjamin-eecs/Theoretical-GMRL

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A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning

https://github.com/Bilibili/LastOrder

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Macro action selection with deep reinforcement learning in StarCraft

https://github.com/CORE-Robotics-Lab/ProLoNets

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Neural-encoding Human Experts' Domain Knowledge to Warm Start Reinforcement Learning

https://github.com/DSobscure/cgi_drl_platform

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An Unsupervised Video Game Playstyle Metric via State Discretization

https://github.com/Div99/IQ-Learn

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IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/FragileTheory/FractalAI

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Fractal AI: A fragile theory of intelligence

https://github.com/Francesco-Sovrano/Combining--experience-replay--with--exploration-by-random-network-distillation-

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Combining Experience Replay with Exploration by Random Network Distillation

https://github.com/Games-and-Simulations/StarAlgo

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StarAlgo: A Squad Movement Planning Library for StarCraft using Monte Carlo Tree Search and Negamax

https://github.com/Games-and-Simulations/sc-docker

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Multi-platform Version of StarCraft: Brood War in a Docker Container: Technical Report

https://github.com/GonQue/gbac

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Learning to Perceive in Deep Model-Free Reinforcement Learning

https://github.com/HTRPOCODES/HTRPO-v2

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Hindsight Trust Region Policy Optimization

https://github.com/Hananel-Hazan/bindsnet

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Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games

https://github.com/HjalmarWijk/bounded-prescience

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Shielding Atari Games with Bounded Prescience

https://github.com/HuTobias/HIGHLIGHTS-LRP

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Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps

https://github.com/Hwhitetooth/lirpg

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On Learning Intrinsic Rewards for Policy Gradient Methods

https://github.com/Hwhitetooth/random_gvfs

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Learning State Representations from Random Deep Action-conditional Predictions

https://github.com/IC3Net/IC3Net

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Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

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

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Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

https://github.com/IouJenLiu/HTS-RL

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High-Throughput Synchronous Deep RL

https://github.com/JohanSamir/revisiting_rainbow

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Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research

https://github.com/KDL-umass/saliency_maps

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Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning

https://github.com/KodirjonAkhmedov/Real-Time-Data-Collection-Dota-2

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Machine learning models for DOTA 2 outcomes prediction

https://github.com/KohlerHECTOR/interpreter-py

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Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning

https://github.com/Lamperougeyxy/GHQ

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GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement Learning

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