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

https://github.com/schroederdewitt/multiagent_mujoco

공식 코드

FACMAC: Factored Multi-Agent Centralised Policy Gradients

https://github.com/shagunsodhani/consistent-dynamics

공식 코드

Learning Powerful Policies by Using Consistent Dynamics Model

https://github.com/sharathraparthy/polynomial-mixing-times

공식 코드

Continual Learning In Environments With Polynomial Mixing Times

https://github.com/shariqiqbal2810/REFIL

공식 코드

Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

https://github.com/shashacks/symmetric_rl

공식 코드

Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales

https://github.com/shayegano/CASL

공식 코드

Crossmodal Attentive Skill Learner

https://github.com/shiwj16/SSINet

공식 코드

Self-Supervised Discovering of Interpretable Features for Reinforcement Learning

https://github.com/sisl/DICG

공식 코드

Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning

https://github.com/sjtu-marl/malib

공식 코드

MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning

https://github.com/sjtuytc/-AAAI21-RoutineAugmentedPolicyLearning-RAPL-

공식 코드

Augmenting Policy Learning with Routines Discovered from a Single Demonstration

https://github.com/sjtuytc/AAAI21-RoutineAugmentedPolicyLearning

공식 코드

Augmenting Policy Learning with Routines Discovered from a Single Demonstration

https://github.com/smerdov/CS_GO_Sensors_Data_Analysis

공식 코드

AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors

https://github.com/smerdov/eSports_Sensors_Dataset

공식 코드

Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

https://github.com/smerdov/smart_chair_client

공식 코드

eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair

https://github.com/somnathhazra/uncertainties_marl

공식 코드

Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics

https://github.com/sowmya-mp/rl_a3c_pytorch

공식 코드

Visual Transfer between Atari Games using Competitive Reinforcement Learning

https://github.com/sri-aic/sc2-feature-extractor

공식 코드

A Framework for Understanding and Visualizing Strategies of RL Agents

https://github.com/srsohn/msgi

공식 코드

Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies

https://github.com/ssg-research/ad3-action-distribution-divergence-detector

공식 코드

Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and Defenses

https://github.com/sureli/laber

공식 코드

Large Batch Experience Replay

https://github.com/szrlee/hyperagent

공식 코드

Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent

https://github.com/tartrl/tikick

공식 코드

TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations

https://github.com/tencent-ailab/tleague_projpage

공식 코드

TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning

https://github.com/tensorflow/tensor2tensor

공식 코드

Model-Based Reinforcement Learning for Atari

https://github.com/teslacool/m-curl

공식 코드

Masked Contrastive Representation Learning for Reinforcement Learning

https://github.com/thaihungle/sm

공식 코드

Beyond Surprise: Improving Exploration Through Surprise Novelty

https://github.com/thanhnguyentang/mmdrl

공식 코드

Distributional Reinforcement Learning via Moment Matching

https://github.com/tiencapham/ife

공식 코드

Pay Attention to What and Where? Interpretable Feature Extractor in Vision-based Deep Reinforcement Learning

https://github.com/tkipf/c-swm

공식 코드

Contrastive Learning of Structured World Models

https://github.com/tmoer/mcts-t

공식 코드

Monte Carlo Tree Search for Asymmetric Trees

https://github.com/tobiaslv/rad

공식 코드

Conformal Symplectic Optimization for Stable Reinforcement Learning

https://github.com/tonghanwang/casec-maco-benchmark

공식 코드

Context-Aware Sparse Deep Coordination Graphs

https://github.com/topwasu/poe-world

공식 코드

PoE-World: Compositional World Modeling with Products of Programmatic Experts

https://github.com/tuomaso/radial_rl

공식 코드

Robust Deep Reinforcement Learning through Adversarial Loss

https://github.com/tuomaso/radial_rl_v2

공식 코드

Robust Deep Reinforcement Learning through Adversarial Loss

https://github.com/uber-research/atari-model-zoo

공식 코드

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents

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