Curriculum Learning for Relative Overgeneralization

by   Lin Shi, et al.

In multi-agent reinforcement learning (MARL), many popular methods, such as VDN and QMIX, are susceptible to a critical multi-agent pathology known as relative overgeneralization (RO), which arises when the optimal joint action's utility falls below that of a sub-optimal joint action in cooperative tasks. RO can cause the agents to get stuck into local optima or fail to solve tasks that require significant coordination between agents within a given timestep. Recent value-based MARL algorithms such as QPLEX and WQMIX can overcome RO to some extent. However, our experimental results show that they can still fail to solve cooperative tasks that exhibit strong RO. In this work, we propose a novel approach called curriculum learning for relative overgeneralization (CURO) to better overcome RO. To solve a target task that exhibits strong RO, in CURO, we first fine-tune the reward function of the target task to generate source tasks that are tailored to the current ability of the learning agent and train the agent on these source tasks first. Then, to effectively transfer the knowledge acquired in one task to the next, we use a novel transfer learning method that combines value function transfer with buffer transfer, which enables more efficient exploration in the target task. We demonstrate that, when applied to QMIX, CURO overcomes severe RO problem and significantly improves performance, yielding state-of-the-art results in a variety of cooperative multi-agent tasks, including the challenging StarCraft II micromanagement benchmarks.


page 1

page 2

page 3

page 4


Regularized Softmax Deep Multi-Agent Q-Learning

Tackling overestimation in Q-learning is an important problem that has b...

StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning

Real-time strategy games have been an important field of game artificial...

Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning

Value-based methods of multi-agent reinforcement learning (MARL), especi...

Deep Coordination Graphs

This paper introduces the deep coordination graph (DCG) for collaborativ...

Learning Reward Machines in Cooperative Multi-Agent Tasks

This paper presents a novel approach to Multi-Agent Reinforcement Learni...

Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems

We introduce a curriculum learning algorithm, Variational Automatic Curr...

From Few to More: Large-scale Dynamic Multiagent Curriculum Learning

A lot of efforts have been devoted to investigating how agents can learn...

Please sign up or login with your details

Forgot password? Click here to reset