The existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments. A typical example is a case of RoboCup...
Over the past few years, swarm based systems have emerged as an attractive paradigm for building large scale distributed systems composed of numerous independent but coordinating ...
Matthew Hoeing, Prithviraj Dasgupta, Plamen V. Pet...
In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of usin...
— The need for reliable maps of subterranean spaces too hazardous for humans to occupy has motivated the use of robotic technology as mapping tools. As such, we present a systemi...
Aaron Morris, David Silver, David I. Ferguson, Sco...
Evolving a robot's sensor morphology along with its control program has the potential to significantly improve its effectiveness in completing the assigned task, plus accommod...