Jayant Singh

• Kumar, Ravi; Dale, Jørgen; Singh, Jayant & Zhou, Jing (2023). Design and Development of an Anthropomorphic Gripper for Service Robotics and Prosthetic Applications. International Conference on Control, Mechatronics and Automation. ISSN 2837-5114. s. 233–238. doi: 10.1109/ICCMA59762.2023.10374949.
• Singh, Jayant; Zhou, Jing & Beferull-Lozano, Baltasar (2023). Enhancing Multi-Agent Reinforcement Learning: Set Function Approximation and Dynamic Policy. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. Vis sammendrag

  While Deep Learning based methods can solve complex problems by employing Neural Networks to act as powerful function approximators, they often suffer from inflexibility in terms of deployment beyond the training scenario and also include irrelevant data priors in the form of an ordered array of input values. This problem is quite evident in the field of MultiAgent Reinforcement Learning (MARL), where most research covers methods that are trained on a fixed number of agents, restricted by the fixed size of the input vector. In this paper, we argue that this is not a reasonable assumption, both in terms of the inflexible amount of environmental information and the restrictive nature of the structure of the information. We explore DeepSets and Set Transformers as two powerful set function approximators to address the problem of cardinality invariance and permutation invariance in the observation space of a reinforcement learning agent. We explain Set-Input Reinforcement Learning (SIRL) in detail and evaluate the performance of the DeepSets and Set Transformer methods through simulated experiments on a challenging multi-agent environment, that otherwise yields sub-optimal policies through traditional function approximation approaches. We demonstrate that both DeepSets and Set Transformer based encoders scale well to increasing the number of agents from training to evaluation.

• Singh, Jayant; Zhou, Jing; Beferull-Lozano, Baltasar & Tyapin, Ilya (2022). Learning Cooperative Multi-Agent Policies with Multi-Channel Reward Curriculum Based Q-Learning. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. doi: 10.1109/IECON49645.2022.9969046.

Se alle arbeider i Cristin

• Zhou, Jing; Kumar, Ravi; Dale, Jørgen & Singh, Jayant (2023). Design and Development of a low-cost Anthropomorphic Gripper for Service Robotics and Prosthetic Applications. Universitetet i Agder.

Se alle arbeider i Cristin