Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
The goal of my research is to understand speech input in a continuous manner by treating the input stream as fragmental utterances. This allows us to use various approaches to pre...
In this paper, we study several linear-time equivalences (Markovian trace equivalence, failure and ready trace equivalence) for continuous-time Markov chains that refer to the pro...
Verena Wolf, Christel Baier, Mila E. Majster-Ceder...
Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
This paper presents a hardware-based dynamic optimizer that continuously optimizes an application’s instruction stream. In continuous optimization, dataflow optimizations are p...
Brian Fahs, Todd M. Rafacz, Sanjay J. Patel, Steve...