DeepAI AI Chat
Log In Sign Up

Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments

by   Yan Zheng, et al.
Tianjin University
Soochow University

Despite single agent deep reinforcement learning has achieved significant success due to the experience replay mechanism, Concerns should be reconsidered in multiagent environments. This work focus on the stochastic cooperative environment. We apply a specific adaptation to one recently proposed weighted double estimator and propose a multiagent deep reinforcement learning framework, named Weighted Double Deep Q-Network (WDDQN). To achieve efficient cooperation, Lenient Reward Network and Mixture Replay Strategy are introduced. By utilizing the deep neural network and the weighted double estimator, WDDQN can not only reduce the bias effectively but also be extended to many deep RL scenarios with only raw pixel images as input. Empirically, the WDDQN outperforms the existing DRL algorithm (double DQN) and multiagent RL algorithm (lenient Q-learning) in terms of performance and convergence within stochastic cooperative environments.


Lenient Multi-Agent Deep Reinforcement Learning

A significant amount of research in recent years has been dedicated towa...

Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control Problem

The adaptive traffic signal control (ATSC) problem can be modeled as a m...

Hierarchical Deep Multiagent Reinforcement Learning

Despite deep reinforcement learning has recently achieved great successe...

The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA

During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with ...

Vanishing Bias Heuristic-guided Reinforcement Learning Algorithm

Reinforcement Learning has achieved tremendous success in the many Atari...

Double Deep Q-Learning in Opponent Modeling

Multi-agent systems in which secondary agents with conflicting agendas a...