This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …

Reinforcement Learning is an area of Machine Learning focused on how agents can be
trained to make sequential decisions, and achieve a particular goal within an arbitrary …

This paper introduces the PettingZoo library and the accompanying Agent Environment
Cycle (" AEC") games model. PettingZoo is a library of diverse sets of multi-agent …

On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed
traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the …

An open problem in autonomous vehicle safety validation is building reliable models of
human driving behavior in simulation. This work presents an approach to learn neural …

Cooperative multi-robot tasks can benefit from heterogeneity in the robots' physical and
behavioral traits. In spite of this, traditional Multi-Agent Reinforcement Learning (MARL) …

The necessity for cooperation among intelligent machines has popularised cooperative multi-
agent reinforcement learning (MARL) in AI research. However, many research endeavours …

Cooperative multi-agent reinforcement learning (MARL) is making rapid progress for solving
tasks in a grid world and real-world scenarios, in which agents are given different attributes …

We present DiNNO, a distributed algorithm that enables a group of robots to collaboratively
optimize a deep neural network model while communicating over a mesh network. Each …

Reinforcement learning (RL) is a widely investigated intelligent algorithm and proved to be
useful in the wireless communication area. However, for optimization problems in large …