Mohamed S. Talamali

Karthik Soma, Mohamed S. Talamali, Genki Miyauchi et al.

In multi-agent systems, should limited resources be concentrated into a few capable agents or distributed among many simpler ones? This work formulates the split over $n$ resource sharing problem where a group of $n$ agents equally shares a common resource (e.g., monetary budget, computational resources, physical size). We present a case study in multi-agent coverage where the area of the disk-shaped footprint of agents scales as $1/n$. A formal analysis reveals that the initial coverage rate grows with $n$. However, if the speed of agents decreases proportionally with their radii, groups of all sizes perform equally well, whereas if it decreases proportionally with their footprints, a single agent performs best. We also present computer simulations in which resource splitting increases the failure rates of individual agents. The models and findings help identify optimal distributiveness levels and inform the design of multi-agent systems under resource constraints.

Reabetswe M. Nkhumise, Mohamed S. Talamali, Aditya Gilra

Reinforcement learning (RL) has enabled major advances in fields such as robotics and natural language processing. A key challenge in RL is measuring task complexity, which is essential for creating meaningful benchmarks and designing effective curricula. While there are numerous well-established metrics for assessing task complexity in tabular settings, relatively few exist in non-tabular domains. These include (i) Statistical analysis of the performance of random policies via Random Weight Guessing (RWG), and (ii) information-theoretic metrics Policy Information Capacity (PIC) and Policy-Optimal Information Capacity (POIC), which are reliant on RWG. In this paper, we evaluate these methods using progressively difficult robotic manipulation setups, with known relative complexity, with both dense and sparse reward formulations. Our empirical results reveal that measuring complexity is still nuanced. Specifically, under the same reward formulation, PIC suggests that a two-link robotic arm setup is easier than a single-link setup - which contradicts the robotic control and empirical RL perspective whereby the two-link setup is inherently more complex. Likewise, for the same setup, POIC estimates that tasks with sparse rewards are easier than those with dense rewards. Thus, we show that both PIC and POIC contradict typical understanding and empirical results from RL. These findings highlight the need to move beyond RWG-based metrics towards better metrics that can more reliably capture task complexity in non-tabular RL with our task framework as a starting point.

Mohamed S. Talamali, Genki Miyauchi, Thomas Watteyne et al.

Unmanned Aerial Vehicles (UAVs) are expected to transform logistics, reducing delivery time, costs, and emissions. This study addresses an on-demand delivery , in which fleets of UAVs are deployed to fulfil orders that arrive stochastically. Unlike previous work, it considers UAVs with heterogeneous, unknown energy storage capacities and assumes no knowledge of the energy consumption models. We propose a decentralised deployment strategy that combines auction-based task allocation with online learning. Each UAV independently decides whether to bid for orders based on its energy storage charge level, the parcel mass, and delivery distance. Over time, it refines its policy to bid only for orders within its capability. Simulations using realistic UAV energy models reveal that, counter-intuitively, assigning orders to the least confident bidders reduces delivery times and increases the number of successfully fulfilled orders. This strategy is shown to outperform threshold-based methods which require UAVs to exceed specific charge levels at deployment. We propose a variant of the strategy which uses learned policies for forecasting. This enables UAVs with insufficient charge levels to commit to fulfilling orders at specific future times, helping to prioritise early orders. Our work provides new insights into long-term deployment of UAV swarms, highlighting the advantages of decentralised energy-aware decision-making coupled with online learning in real-world dynamic environments.