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   https://doi.org/10.1109/IPC53466.2022.9975540  

   Fardoost, Alireza; Vanani, Fatemeh Ghaedi; Zhu, Zheyuan; Doerr, Christopher; Pang, Shuo; Li, Guifang (November 2022, IEEE Photonics Conference)

   We propose a coherent multi-dimensional (wavelength, spatial mode, polarization, etc.) photonic tensor accelerator capable of matrix-vector, matrix-matrix, and batch matrix multiplications in a single clock cycle. A proof-of-concept 2x2 matrix-matrix multiplication at 25GBd with 4.67 bit precision was experimentally demonstrated. 

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4. A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans

   Peng, Bei; MacGlashan, James; Loftin, Robert; Littman, Michael L.; Roberts, David L.; Taylor, Matthew E. (January 2016, AAMAS)

   As robots become pervasive in human environments, it is important to enable users to effectively convey new skills without programming. Most existing work on Interactive Reinforcement Learning focuses on interpreting and incorporating non-expert human feedback to speed up learning; we aim to design a better representation of the learning agent that is able to elicit more natural and effective communication between the human trainer and the learner, while treating human feedback as discrete communication that depends probabilistically on the trainer’s target policy. This work entails a user study where participants train a virtual agent to accomplish tasks by giving reward and/or punishment in a variety of simulated environments. We present results from 60 participants to show how a learner can ground natural language commands and adapt its action execution speed to learn more efficiently from human trainers. The agent’s action execution speed can be successfully modulated to encourage more explicit feedback from a human trainer in areas of the state space where there is high uncertainty. Our results show that our novel adaptive speed agent dominates different fixed speed agents on several measures of performance. Additionally, we investigate the impact of instructions on user performance and user preference in training conditions. 

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5. A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans

   Peng, Bei; MacGlashan, James; Loftin, Robert; Littman, Michael L.; Roberts, David L.; Taylor, Matthew E. (July 2016, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems)

   As robots become pervasive in human environments, it is important to enable users to effectively convey new skills without programming. Most existing work on Interactive Reinforcement Learning focuses on interpreting and incorporating non-expert human feedback to speed up learning; we aim to design a better representation of the learning agent that is able to elicit more natural and effective communication between the human trainer and the learner, while treating human feedback as discrete communication that depends probabilistically on the trainer's target policy. This work entails a user study where participants train a virtual agent to accomplish tasks by giving reward and/or punishment in a variety of simulated environments. We present results from 60 participants to show how a learner can ground natural language commands and adapt its action execution speed to learn more efficiently from human trainers. The agent's action execution speed can be successfully modulated to encourage more explicit feedback from a human trainer in areas of the state space where there is high uncertainty. Our results show that our novel adaptive speed agent dominates different fixed speed agents on several measures of performance. Additionally, we investigate the impact of instructions on user performance and user preference in training conditions. 

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