predictive q-routing
Predictive Q-Routing: A Memory-based Reinforcement Learning Approach to Adaptive Traffic Control
In this paper, we propose a memory-based Q-Iearning algorithm called predictive Q-routing (PQ-routing) for adaptive traffic con(cid:173) trol. We attempt to address two problems encountered in Q-routing (Boyan & Littman, 1994), namely, the inability to fine-tune rout(cid:173) ing policies under low network load and the inability to learn new optimal policies under decreasing load conditions. Unlike other memory-based reinforcement learning algorithms in which mem(cid:173) ory is used to keep past experiences to increase learning speed, PQ-routing keeps the best experiences learned and reuses them by predicting the traffic trend. The effectiveness of PQ-routing has been verified under various network topologies and traffic con(cid:173) ditions. Simulation results show that PQ-routing is superior to Q-routing in terms of both learning speed and adaptability.
Predictive Q-Routing: A Memory-based Reinforcement Learning Approach to Adaptive Traffic Control
Choi, Samuel P. M., Yeung, Dit-Yan
The controllers usually have no or only very little prior knowledge of the environment. While only local communication between controllers is allowed, the controllers must cooperate among themselves to achieve the common, global objective. Finding the optimal routing policy in such a distributed manner is very difficult. Moreover, since the environment is non-stationary, the optimal policy varies with time as a result of changes in network traffic and topology.
Predictive Q-Routing: A Memory-based Reinforcement Learning Approach to Adaptive Traffic Control
Choi, Samuel P. M., Yeung, Dit-Yan
The controllers usually have no or only very little prior knowledge of the environment. While only local communication between controllers is allowed, the controllers must cooperate among themselves to achieve the common, global objective. Finding the optimal routing policy in such a distributed manner is very difficult. Moreover, since the environment is non-stationary, the optimal policy varies with time as a result of changes in network traffic and topology.
Predictive Q-Routing: A Memory-based Reinforcement Learning Approach to Adaptive Traffic Control
Choi, Samuel P. M., Yeung, Dit-Yan
The controllers usually have no or only very little prior knowledge of the environment. While only local communication between controllers is allowed, the controllers must cooperate among themselves to achieve the common, global objective. Finding the optimal routing policy in such a distributed manner is very difficult. Moreover, since the environment is non-stationary, the optimal policy varies with time as a result of changes in network traffic and topology.