29 citations · 70 across the 3 of their papers we have counts for
4 papers · 1 filter
Active Imitation Learning via Reduction to I.I.D. Active Learning
Kshitij Judah, Alan Fern, Thomas G. Dietterich
In standard passive imitation learning, the goal is to learn a target policy by passively observing full execution trajectories of it. Unfortunately, generating such trajectories c…
Learning from Sparse Data by Exploiting Monotonicity Constraints
Eric E. Altendorf, Angelo C. Restificar, Thomas G. Dietterich
When training data is sparse, more domain knowledge must be incorporated into the learning algorithm in order to reduce the effective size of the hypothesis space. This paper build…
State Abstraction in MAXQ Hierarchical Reinforcement Learning
Thomas G. Dietterich
Many researchers have explored methods for hierarchical reinforcement learning (RL) with temporal abstractions, in which abstract actions are defined that can perform many primitiv…
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
Thomas G. Dietterich
This paper presents the MAXQ approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and deco…