4 accepted papers
We consider the batch reinforcement learning problem where the agent needs to learn only from a fixed batch of data, without further interaction with the environment. In such a scenario, we want to prevent the optimized policy from deviating too much from the data collection policy since the estimat…
In order to solve complex problems an agent must be able to reason over a sufficiently long horizon. Temporal abstraction, commonly modeled through options, offers the ability to reason at many timescales, but the horizon length is still determined by the discount factor of the underlying Markov Dec…
We propose a reinforcement learning (RL) algorithm that uses mutual-information regularization to optimize a prior action distribution for better performance and exploration. Entropy-based regularization has previously been shown to improve both exploration and robustness in challenging sequential d…
Accurate and reliable event prediction is imperative for supporting movement with an exoskeleton. Two events are important during a sit-to-stand movement: seat-off, the event at which the subject leaves the chair and start-of-assistance for hip and knee, the earliest time at which assistance may be