20 citations · 53 across the 15 of their papers we have counts for
4 papers · 1 filter
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…