PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm

Multi-objective reinforcement learning (MORL) approaches have emerged to tackle many real-world problems with multiple conflicting objectives by maximizing a joint objective function weighted by a preference vector. These approaches find fixed customized policies corresponding to preference vectors specified during training. However, the design constraints and objectives typically change dynamically in real-life scenarios. Furthermore, storing a policy for each potential preference is not scalable. Hence, obtaining a set of Pareto front solutions for the entire preference space in a given domain with a single training is critical. To this end, we propose a novel MORL algorithm that trains a single universal network to cover the entire preference space scalable to continuous robotic tasks. The proposed approach, Preference-Driven MORL (PD-MORL), utilizes the preferences as guidance to update the network parameters. It also employs a novel parallelization approach to increase sample efficiency. We show that PD-MORL achieves up to 25% larger hypervolume for challenging continuous control tasks and uses an order of magnitude fewer trainable parameters compared to prior approaches.Comment: 24 pages, 8 Figures, 9 Tables, Published as a conference paper at ICLR 2023, https://openreview.net/forum?id=zS9sRyaPFl

Physical-aware link allocation and route assignment for chip multiprocessing

The architecture definition, design, and validation of the interconnect networks is a key step in the design of modern on-chip systems. This paper proposes a mathematical formulation of the problem of simultaneously defining the topology of the network and the message routes for the traffic among the processing elements of the system. The solution of the problem meets the physical and performance constraints defined by the designer. The method guarantees that the generated solution is deadlock free. It is also capable of automatically discovering topologies that have been previously used in industrial systems. The applicability of the method has been validated by solving realistic size interconnect networks modeling the typical multiprocessor systems.Peer ReviewedPostprint (published version