Papers with Code 아카이브

Video Games

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

6

평가표

0

보존된 평가 기록

0

코드

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

https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

공식 코드

Soft Actor-Critic for Discrete Action Settings

https://github.com/paleroy/competpymarl

공식 코드

Value-based CTDE Methods in Symmetric Two-team Markov Game: from Cooperation to Team Competition

https://github.com/paleroy/competsmac

공식 코드

Value-based CTDE Methods in Symmetric Two-team Markov Game: from Cooperation to Team Competition

https://github.com/paperwithcode/pop3d

공식 코드

Policy Optimization With Penalized Point Probability Distance: An Alternative To Proximal Policy Optimization

https://github.com/pierthodo/temporal_regularization

공식 코드

Temporal Regularization in Markov Decision Process

https://github.com/pmalekzadeh/A-robust-quantile-huber-loss

공식 코드

A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learning

https://github.com/prabhath-r/FDQN_RL

공식 코드

FDQN: A Flexible Deep Q-Network Framework for Game Automation

https://github.com/prashanm/StarCraft-II-causal-explanations

공식 코드

Explainable Reinforcement Learning Through a Causal Lens

https://github.com/princeton-nlp/il-scaling-in-games

공식 코드

Scaling Laws for Imitation Learning in Single-Agent Games

https://github.com/puyuan1996/MARL

공식 코드

Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning

https://github.com/qb3/discrating

공식 코드

On the Limitations of Elo: Real-World Games, are Transitive, not Additive

https://github.com/qgallouedec/lge

공식 코드

Cell-Free Latent Go-Explore

https://github.com/qq456cvb/doudizhu-C

공식 코드

Combinational Q-Learning for Dou Di Zhu

https://github.com/quantumiracle/mars

공식 코드

A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

https://github.com/quantumiracle/nash-dqn

공식 코드

A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

https://github.com/ramsayxiaoshao/SwarmBrain-Embodied-agent-for-real-time-strategy-game-StarCraft-II-via-large-language-models

공식 코드

SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language models

https://github.com/reinholdm/offline-pre-trained-multi-agent-decision-transformer

공식 코드

Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks

https://github.com/rezabonyadi/atari_rl_arxiv

공식 코드

Self Punishment and Reward Backfill for Deep Q-Learning

https://github.com/richoux/microrts-uncertainty

공식 코드

Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games

https://github.com/richoux/taunt

공식 코드

Terrain Analysis in StarCraft 1 and 2 as Combinatorial Optimization

https://github.com/ringbdstack/sr-marl

공식 코드

Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles

https://github.com/rl-interpretation/understandingRL

공식 코드

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

https://github.com/rl-submissions/macro-credit-masp

공식 코드

Meta-learning how to Share Credit among Macro-Actions

https://github.com/rlglab/minizero

공식 코드

MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games

https://github.com/rlglab/optionzero

공식 코드

OptionZero: Planning with Learned Options

https://github.com/rll-research/url_benchmark

공식 코드

Behavior From the Void: Unsupervised Active Pre-Training

https://github.com/rllab/rllab

공식 코드

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/robinzixuan/IQN_Agent

공식 코드

IGN : Implicit Generative Networks

https://github.com/rubenrtorrado/GVGAI_GYM

공식 코드

Deep Reinforcement Learning for General Video Game AI

https://github.com/sacktock/ambs

공식 코드

Approximate Model-Based Shielding for Safe Reinforcement Learning

https://github.com/sahiljain11/audio_rl

공식 코드

Prosody as a Teaching Signal for Agent Learning: Exploratory Studies and Algorithmic Implications

https://github.com/saizhang0218/VBC

공식 코드

Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control

https://github.com/samlobel/cfn

공식 코드

Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning

https://github.com/samuelschmidgall/evolutionaryselfreplication

공식 코드

Evolutionary Self-Replication as a Mechanism for Producing Artificial Intelligence

https://github.com/schmidtdominik/rainbow

공식 코드

Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari

https://github.com/schroederdewitt/mackrl

공식 코드

Multi-Agent Common Knowledge Reinforcement Learning

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