Publications

Honey, I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL

Published in (*To appear*) 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". arXiv preprint arXiv:2102.11893. https://arxiv.org/abs/2102.11893

Regularizing Action Policies for Smooth Control with Reinforcement Learning

Published in (*To Appear*) 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

How to Train your Quadrotor: A Framework for Consistently Smooth and Responsive Flight Control via Reinforcement Learning

Published in (*To Appear*) 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". arXiv preprint arXiv:2012.06656. https://arxiv.org/abs/2012.06656

[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