157 citations · 778 across the 15 of their papers we have counts for
15 papers
Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL
Dillon Sandhu, Ronald Parr
We revisit a classic "chicken-and-egg" problem in reinforcement learning: to safely improve a policy, the value function must be accurate on the state-visitation distribution of th…
Fitted Q-Learning for Relational Domains
Srijita Das, Sriraam Natarajan, Kaushik Roy +2
We consider the problem of Approximate Dynamic Programming in relational domains. Inspired by the success of fitted Q-learning methods in propositional settings, we develop the fir…
Deep Radial-Basis Value Functions for Continuous Control
Kavosh Asadi, Neev Parikh, Ronald E. Parr +2
A core operation in reinforcement learning (RL) is finding an action that is optimal with respect to a learned value function. This operation is often challenging when the learned…
On the Existence of Simpler Machine Learning Models
Lesia Semenova, Cynthia Rudin, Ronald Parr
It is almost always easier to find an accurate-but-complex model than an accurate-yet-simple model. Finding optimal, sparse, accurate models of various forms (linear models with in…
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
Zhao Song, Ronald E. Parr, Lawrence Carin
The impact of softmax on the value function itself in reinforcement learning (RL) is often viewed as problematic because it leads to sub-optimal value (or Q) functions and interfer…
Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems
Ron Parr
This paper presents two new approaches to decomposing and solving large Markov decision problems (MDPs), a partial decoupling method and a complete decoupling method. In these appr…