107 citations · 390 across the 26 of their papers we have counts for
6 papers · 1 filter
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…
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback
Han Shao, Lee Cohen, Avrim Blum +3
In classic reinforcement learning (RL) and decision making problems, policies are evaluated with respect to a scalar reward function, and all optimal policies are the same with reg…
Concurrent Training Improves the Performance of Behavioral Cloning from Observation
Zachary W. Robertson, Matthew R. Walter
Learning from demonstration is widely used as an efficient way for robots to acquire new skills. However, it typically requires that demonstrations provide full access to the state…
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…
Multigrid Neural Memory
Tri Huynh, Michael Maire, Matthew R. Walter
We introduce a novel approach to endowing neural networks with emergent, long-term, large-scale memory. Distinct from strategies that connect neural networks to external memory ban…