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

https://github.com/bjliaa/c2d

공식 코드

Conjugated Discrete Distributions for Distributional Reinforcement Learning

https://github.com/brett-daley/dqn-lambda

공식 코드

Reconciling $λ$-Returns with Experience Replay

https://github.com/brett-daley/virtual-replay-cache

공식 코드

Virtual Replay Cache

https://github.com/burcukoglu/p4o-predictiveprocessingppo

공식 코드

Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization

https://github.com/cair/DeepRTS

공식 코드

Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games

https://github.com/camel-ai/vlm-play-starcraft2

공식 코드

AVA: Attentive VLM Agent for Mastering StarCraft II

https://github.com/cameronangliss/vgc-bench

공식 코드

A Benchmark for Generalizing Across Diverse Team Strategies in Competitive Pokémon

https://github.com/carolinewang01/dm2

공식 코드

DM$^2$: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching

https://github.com/carolinewang01/naht

공식 코드

N-Agent Ad Hoc Teamwork

https://github.com/cathyhxh/ctds

공식 코드

CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning

https://github.com/chaovven/SMIX

공식 코드

SMIX($λ$): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning

https://github.com/chitaapc/dota2_beyondthemeta_characterrepresentation

공식 코드

Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analytics

https://github.com/chitaapc/dota2ccr

공식 코드

Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analytics

https://github.com/cjreinforce/jowa

공식 코드

Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining

https://github.com/coco66/ADFQ

공식 코드

Assumed Density Filtering Q-learning

https://github.com/colazhang22/pps-qmix

공식 코드

PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning

https://github.com/coldsummerday/SD-SAC

공식 코드

Revisiting Discrete Soft Actor-Critic

https://github.com/conglu1997/intelligent-go-explore

공식 코드

Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models

https://github.com/cooper-org/cooper

공식 코드

Active Reinforcement Learning for Robust Building Control

https://github.com/cranial-xix/marl-copa

공식 코드

Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition

https://github.com/cugbbaiyun/hgcn-mix

공식 코드

Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution

https://github.com/cxxgtxy/POP3D

공식 코드

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

https://github.com/daanklijn/neurocoevolution

공식 코드

A coevolutionary approach to deep multi-agent reinforcement learning

https://github.com/danieldugas/navdreams

공식 코드

NavDreams: Towards Camera-Only RL Navigation Among Humans

https://github.com/danieleugenioneves/comper-release-results

공식 코드

Improving Experience Replay through Modeling of Similar Transitions' Sets

https://github.com/danijar/dreamerv2

공식 코드

Mastering Atari with Discrete World Models

https://github.com/datake/sinkhorndistrl

공식 코드

Distributional Reinforcement Learning with Regularized Wasserstein Loss

https://github.com/deepmind/deepmind-research/tree/master/perceiver

공식 코드

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/deepmind/deepmind-research/tree/master/rl_unplugged

공식 코드

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

https://github.com/deepmind/diplomacy

공식 코드

Learning to Play No-Press Diplomacy with Best Response Policy Iteration

https://github.com/deepmind/pushworld

공식 코드

PushWorld: A benchmark for manipulation planning with tools and movable obstacles

https://github.com/deepmind/pysc2

공식 코드

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/deepmind/scalable_agent

공식 코드

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/deepmind/spectral_inference_networks

공식 코드

Spectral Inference Networks: Unifying Deep and Spectral Learning

https://github.com/deepsense-ai/Distributed-BA3C

공식 코드

Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes

https://github.com/demosthen/activerl

공식 코드

Active Reinforcement Learning for Robust Building Control

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