A Multi-Agent Reinforcement Learning Approach to Path Selection in Optical Burst Switching Networks

An important issue of research in optical burst switching (OBS) networks is to minimize the loss of bursts due to contention at the intermediate nodes. These contention losses can be minimized with the design of efficient path selection algorithms at the ingress node. Path selection algorithms that learn the optimal path dynamically with the changing traffic conditions outperform the deterministic path selection algorithms. Usually in the single agent path selection algorithms, a path is selected by the agent based on the feedback received at the ingress node which does not capture the effect of the paths selected by the other nodes in the network. We develop a multi-agent approach for path selection that includes the effect of the selection made by all the other nodes in the network. The proposed path selection algorithm uses agents at different source nodes to collectively learn the network dynamics and select the best outgoing path for each burst. We present simulation results to demonstrate the effectiveness of the proposed algorithm over the other similar algorithms in the literature.