MASER: Multi-Agent Reinforcement Learning with Subgoals
What it is
This repository provides a Python implementation of the MASER algorithm, a multi-agent reinforcement learning method that generates subgoals from an experience replay buffer. It is designed to work with the StarCraft II Multi-Agent Challenge (SMAC) environment and uses QMIX as the underlying architecture.
The problem it solves
Multi-agent reinforcement learning often suffers from credit assignment and non-stationarity; MASER addresses this by generating subgoals from experience to guide agent learning.
What you could use it for
- Researching multi-agent reinforcement learning algorithms
- Training agents in StarCraft II Multi-Agent Challenge scenarios
- Implementing hierarchical reinforcement learning with subgoal generation
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.