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publications
(preprint) Reward-guided Curriculum for Robust Reinforcement Learning
Published in (*preprint*), 2019
We propose a novel method to develop robust action policies using an automated curriculum which seeks to improve task generalization and reduce policy brittleness.
Recommended citation: Mysore, S., Platt, R., Saenko, K. (2019). "Reward-guided Curriculum for Robust Reinforcement Learning." http://sidmysore.github.io/files/RgC_paper_Preprint.pdf
[Workshop paper] Reward-guided Curriculum for Learning Robust Action Policies
Published in Workshop on Multi-task and Lifelong Reinforcement Learning at ICML 2019, 2019
We propose a novel method to develop robust actor policies, by automatically developing a sampling curriculum over environment settings to use in training
Recommended citation: Mysore, S., Platt, R., Saenko, K. (2019). "Reward-guided Curriculum for Learning Robust Action Policies.", Workshop on Multi-task and Lifelong Reinforcement Learning at ICML 2019, Long beach CA http://sidmysore.github.io/files/RgC_MTLRL_Workshop_ICML_2019.pdf
How to Train your Quadrotor: A Framework for Consistently Smooth and Responsive Flight Control via Reinforcement Learning
Published in ACM Transactions on Cyber-Physical Systems, 2021
To combat issues of instability in RL agents, we propose a systematic framework, REinforcement-based transferable Agents through Learning (RE+AL), for designing simulated training environments which preserve the quality of trained agents when transferred to real platforms.
Recommended citation: Mysore, S., Mabsout, B., Saenko, K., & Mancuso, R. (2021). "How to Train your Quadrotor: A Framework for Consistently Smooth and Responsive Flight Control via Reinforcement Learning". ACM Trans. Cyber-Phys. Syst. 5, 4, Article 36 (October 2021), 24 pages. DOI:https://doi.org/10.1145/3466618 https://arxiv.org/abs/2012.06656
Regularizing Action Policies for Smooth Control with Reinforcement Learning
Published in IEEE International Conference on Robotics and Automation (ICRA), 2021
We introduce Conditioning for Action Policy Smoothness (CAPS), an effective yet intuitive regularization on action policies, which offers consistent improvement in the smoothness of the learned state-to-action mappings of neural network controllers, reflected in the elimination of high-frequency components in the control signal. Project Website: http://ai.bu.edu/caps/
Recommended citation: Mysore, S., Mabsout, B., Mancuso, R., & Saenko, K. (2021). "Regularizing Action Policies for Smooth Control with Reinforcement Learning", IEEE International Conference on Robotics and Automation 2021, Xian, China. https://arxiv.org/abs/2012.06644
Honey, I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL
Published in IEEE Conference on Games, 2021
By relaxing the assumption of architectural symmetry, it is often possible for smaller actors to achieve comparable policy performance to their symmetric counterparts. Our experiments show up to 97% reduction in the number of network weights with an average reduction of 77% over multiple algorithms on multiple tasks. Project Website: http://ai.bu.edu/littleActor/
Recommended citation: Mysore, S., Mabsout, B., Mancuso, R., & Saenko, K. (2021). "Honey, I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL"IEEE Conference on Games 2021 https://arxiv.org/abs/2102.11893
Designing Composites with Target Effective Young’s Modulus using Reinforcement Learning
Published in ACM Symposium on Computational Fabrication, 2021
In this work, we develop and utilize a Reinforcement learning (RL)-based framework for the design of composite structures which avoids the need for user-selected training data. Models can be trained using just 2.78% of the total design space and are capable of finding designs at a success rate exceeding 90%.
Recommended citation: Gongora, A.E., Mysore, S., Li, B., Shou, W., Matusik, W., Morgan, E.F., Brown, K.A., & Whiting, E. "Designing Composites with Target Effective Youngs Modulus using Reinforcement Learning". ACM Symposium on Computational Fabrication https://arxiv.org/abs/2110.05260
Multi-Critic Actor Learning: Teaching RL Policies to Act with Style
Published in International Conference on Learning Representations, 2022
Using a single value function (critic) shared over multiple tasks in Actor-Critic multi-task reinforcement learning (MTRL) can result in negative interference between tasks, which can compromise learning performance. Multi-Critic Actor Learning (MultiCriticAL) proposes instead maintaining separate critics for each task being trained while training a single multi-task actor.
Recommended citation: Mysore, S., Cheng, G., Zhao, Y., Saenko, K, & Wu, M. "Multi-Critic Actor Learning: Teaching RL Policies to Act with Style". International Conference on Learning Representations https://openreview.net/forum?id=rJvY_5OzoI
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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