Hospital staff and resources allocation (HSRA) is a critical challenge in healthcare systems,
as it involves balancing the demands of patients, the availability of resources, and the need …

Deep neural networks provide Reinforcement Learning (RL) powerful function
approximators to address large-scale decision-making problems. However, these …

Both Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) have demonstrated
powerful capabilities in policy search with different principles. A promising direction is to …

In reinforcement learning, it is crucial to have an accurate environment dynamics model to
evaluate different policies' value in downstream tasks like offline policy optimization and …

Shape-shifting robots are the feasible solutions to solve the Complete Coverage Planning
(CCP) problem. These robots can extend the covered areas by reconfiguring their shape to …

Recurrent Neural Networks (RNNs) are general-purpose parallel-sequential computers. The
program of an RNN is its weight matrix. How to learn useful representations of RNN weights …

Decision making demands intricate interplay between perception, memory, and reasoning to
discern optimal policies. Conventional approaches to decision making face challenges …

Deep reinforcement learning (DRL) has achieved notable success in continuous control
tasks. However, it faces challenges that limit its applicability to a wider array of tasks …

Learning to evaluate and improve policies is a core problem of Reinforcement Learning
(RL). Traditional RL algorithms learn a value function defined for a single policy. A recently …

We present a representation-driven framework for reinforcement learning. By representing
policies as estimates of their expected values, we leverage techniques from contextual …