1 citations · 1 across the 3 of their papers we have counts for
5 papers
Reinforcement Learning with Depreciating Assets
Taylor Dohmen, Ashutosh Trivedi
A basic assumption of traditional reinforcement learning is that the value of a reward does not change once it is received by an agent. The present work forgoes this assumption and…
Composing Copyless Streaming String Transducers
Rajeev Alur, Taylor Dohmen, Ashutosh Trivedi
Streaming string transducers (SSTs) implement string-to-string transformations by reading each input word in a single left-to-right pass while maintaining fragments of potential ou…
Inferring Probabilistic Reward Machines from Non-Markovian Reward Processes for Reinforcement Learning
Taylor Dohmen, Noah Topper, George Atia +3
The success of reinforcement learning in typical settings is predicated on Markovian assumptions on the reward signal by which an agent learns optimal policies. In recent years, th…
Discounting the Past
Taylor Dohmen, Ashutosh Trivedi
Stochastic games with discounted payoff, introduced by Shapley, model adversarial interactions in stochastic environments where two players try to optimize a discounted sum of rewa…
Regular Model Checking with Regular Relations
Vrunda Dave, Taylor Dohmen, Shankara Narayana Krishna +1
Regular model checking is an exploration technique for infinite state systems where state spaces are represented as regular languages and transition relations are expressed using r…