phil610351 Goto Github PK

marl_agent_and_env

IQL, QMIX, VDN, COMA, QTRAN (QTRAN-Base and QTRAN-Alt), MAVEN, CommNet, DYMA-Cl, G2ANet, and MADDPG

maro

Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.

master-electric_vehicle_charging_recommendation

mava

A library of multi-agent reinforcement learning components and systems

mcpp-based-on-maddpg

created an environment of 10*10 grid and 4 UAVs to carry out coverage path planning cooperatively

md-maddpg

mec

PSO和蚁群算法解决MEC计算卸载问题

mec_alloc_rl

User allocation in Mobile Edge Computing Environment using Reinforcement Learning

mec_offloading

采用强化学习来实现计算卸载

mec_offloading-1

Code for Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market paper https://www.mdpi.com/1999-5903/11/5/118/pdf

mec_simulator

Differential Pricing-based Task Offloading for Delay-Sensitive IoT Applications in Mobile Edge Computing System

mec_ver_new_vehicles

meta-maddpg

meta-MADDPG (Python implementation)

meta-mapg

Source code for "A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning" (ICML 2021)

mfrl

Mean Field Multi-Agent Reinforcement Learning

mimo-noma_aoi

mita

ml-rl-simulations

A multi-layer guided reinforcement learning-based tasks offloading in edge computing - Simulations

mobile-edge-computing

研究生论文

mobile-edge-computing-pyt

mobile-env

An open, minimalist Gym environment for autonomous coordination in wireless mobile networks.

model-based-marl

model-based-meta-rl-for-flight

Codebase for Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads paper. Website: https://sites.google.com/view/meta-rl-for-flight.

modeling

Joint Cache Placement and Request Routing Optimization in Heterogeneous Cellular Networks

modular-rl

[ICML 2020] PyTorch Code for "One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control"

mra

Code for paper "Learning Meta Representation for Agents in Multi-Agent Reinforcement Learning".

multi-agent-deep-q-network-with-stochastic-prioritized-replay

multi-agent-distributed-ppo-traffc-light-control

multi agent RL for traffic light control in Sumo using distributed PPO

multi-agent-fanet-offload

FANET optimization to provide network services to remote areas

multi-agent-mec_offloading-use_drl

使用Drl来解决多智能体卸载问题