1 citations · 1 across the 3 of their papers we have counts for
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Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning
David Yunis, Justin Jung, Falcon Dai +1
Exploration in sparse-reward reinforcement learning is difficult due to the requirement of long, coordinated sequences of actions in order to achieve any reward. Moreover, in conti…
On Reward Structures of Markov Decision Processes
Falcon Z. Dai
A Markov decision process can be parameterized by a transition kernel and a reward function. Both play essential roles in the study of reinforcement learning as evidenced by their…
Loop Estimator for Discounted Values in Markov Reward Processes
Falcon Z. Dai, Matthew R. Walter
At the working heart of policy iteration algorithms commonly used and studied in the discounted setting of reinforcement learning, the policy evaluation step estimates the value of…
Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards
Falcon Z. Dai, Matthew R. Walter
We propose a new complexity measure for Markov decision processes (MDPs), the maximum expected hitting cost (MEHC). This measure tightens the closely related notion of diameter [JO…