Reinforcement Learning Based Hierarchical Multi-Agent Robotic Search Team in Uncertain Environment

Abstract

Field of robotics has been under the limelight because of recent advances in Artificial Intelligence (AI). Due to increased diversity in multi-agent systems, new models are being developed to handle complexity of such systems. However, most of these models do not address problems such as; uncertainty handling, efficient learning, agent coordination and fault detection. This paper presents a novel approach of implementing Reinforcement Learning (RL) on hierarchical robotic search teams. The proposed algorithm handles uncertainties in the system by implementing Q-learning and depicts enhanced efficiency as well as better time consumption compared to prior models. The reason for that is each agent can take action on its own thus there is less dependency on leader agent for RL policy. The performance of this algorithm is measured by introducing agents in an unknown environment with both Markov Decision Process (MDP) and RL policies at their disposal. Simulation-based comparison of the agent motion is presented using the results from of MDP and RL policies. Furthermore, qualitative comparison of the proposed model with prior models is also presented.

HVDC Transmission Line Fault Identification: A Learning Based UAV Control Strategy

“…As aforementioned, leader agent is considered as commandand-control center and local agents are UAVs. The resulting policy is optimized through the value iteration algorithm [53] using the transition probabilities and reward functions defined in previous section. The value iteration equation which gives the optimal solution is given as:…”

“…The approach proposed in this work has been considered through modifications in the work performed in [53] implementing multi-agent systems. The system includes an implementation of leader (command and control center) and local agents (UAVs) approach.…”