16 citations · 25 across the 2 of their papers we have counts for
6 papers
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function
Gellért Weisz, Philip Amortila, Barnabás Janzer +3
We consider local planning in fixed-horizon MDPs with a generative model under the assumption that the optimal value function lies close to the span of a feature map. The generativ…
A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting
Philip Amortila, Nan Jiang, Tengyang Xie
Recently, Wang et al. (2020) showed a highly intriguing hardness result for batch reinforcement learning (RL) with linearly realizable value function and good feature coverage in t…
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions
Gellért Weisz, Philip Amortila, Csaba Szepesvári
We consider the problem of local planning in fixed-horizon and discounted Markov Decision Processes (MDPs) with linear function approximation and a generative model under the assum…
Constrained Markov Decision Processes via Backward Value Functions
Harsh Satija, Philip Amortila, Joelle Pineau
Although Reinforcement Learning (RL) algorithms have found tremendous success in simulated domains, they often cannot directly be applied to physical systems, especially in cases w…
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
Philip Amortila, Doina Precup, Prakash Panangaden +1
We present a distributional approach to theoretical analyses of reinforcement learning algorithms for constant step-sizes. We demonstrate its effectiveness by presenting simple and…
Learning Graph Weighted Models on Pictures
Philip Amortila, Guillaume Rabusseau
Graph Weighted Models (GWMs) have recently been proposed as a natural generalization of weighted automata over strings and trees to arbitrary families of labeled graphs (and hyperg…