A multiagent architecture for concurrent reinforcement learning

In this paper we propose a multiagent architecture for implementing concurrent reinforcement learning, an approach where several agents, sharing the same environment, perceptions and actions, work towards one only objective: learning a single value function. We present encouraging experimental results derived from the initial phase of our research on the combination of concurrent reinforcement learning and learning from demonstration.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Victor Uc Cetina
• Keywords: Concurrent Reinforcement Learning, ESANN 2006, ESANN 2007, Multiagent Architecture, Single Value Function