Rupali Bhati
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View article: Use of an Integrated Knowledge Translation Approach to Develop an Electronic Patient-Reported Outcome System for Cancer Rehabilitation: Tutorial
Use of an Integrated Knowledge Translation Approach to Develop an Electronic Patient-Reported Outcome System for Cancer Rehabilitation: Tutorial Open
Electronic prospective surveillance models (ePSMs) have the potential to improve the management of cancer-related impairments by systematically screening patients using electronic patient-reported outcomes during and after treatment, and l…
View article: Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning
Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Open
Value function decomposition methods for cooperative multi-agent reinforcement learning compose joint values from individual per-agent utilities, and train them using a joint objective. To ensure that the action selection process between i…
View article: On Stateful Value Factorization in Multi-Agent Reinforcement Learning
On Stateful Value Factorization in Multi-Agent Reinforcement Learning Open
Value factorization is a popular paradigm for designing scalable multi-agent reinforcement learning algorithms. However, current factorization methods make choices without full justification that may limit their performance. For example, t…
View article: Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning Open
While there has been significant progress in curriculum learning and continuous learning for training agents to generalize across a wide variety of environments in the context of single-agent reinforcement learning, it is unclear if these …
View article: Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients
Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients Open
Cancer treatment is an arduous process for patients and causes many side-effects during and post-treatment. The treatment can affect almost all body systems and result in pain, fatigue, sleep disturbances, cognitive impairments, etc. These…
View article: CARL: Conditional-value-at-risk Adversarial Reinforcement Learning.
CARL: Conditional-value-at-risk Adversarial Reinforcement Learning. Open
In this paper we present a risk-averse reinforcement learning (RL) method called Conditional value-at-risk Adversarial Reinforcement Learning (CARL). To the best of our knowledge, CARL is the first game formulation for Conditional Value-at…
View article: A Reinforcement Learning Approach to Jointly Adapt Vehicular Communications and Planning for Optimized Driving
A Reinforcement Learning Approach to Jointly Adapt Vehicular Communications and Planning for Optimized Driving Open
Our premise is that autonomous vehicles must optimize communications and motion planning jointly. Specifically, a vehicle must adapt its motion plan staying cognizant of communications rate related constraints and adapt the use of communic…