1 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
Memory-Computation Tradeoffs in Semi Amortized Parametric Optimization
Shijie Pan, Agustin Castellano, Zeyu Shen +1
Learning-enabled decision systems often use offline data or computation to reduce online compute cost. Despite the empirical success of such approaches, there is limited general un…
Learning safety critics via a non-contractive binary bellman operator
Agustin Castellano, Hancheng Min, Juan Andrés Bazerque +1
The inability to naturally enforce safety in Reinforcement Learning (RL), with limited failures, is a core challenge impeding its use in real-world applications. One notion of safe…
Reinforcement Learning with Almost Sure Constraints
Agustin Castellano, Hancheng Min, Juan Bazerque +1
In this work we address the problem of finding feasible policies for Constrained Markov Decision Processes under probability one constraints. We argue that stationary policies are…
Assured RL: Reinforcement Learning with Almost Sure Constraints
Agustin Castellano, Juan Bazerque, Enrique Mallada
We consider the problem of finding optimal policies for a Markov Decision Process with almost sure constraints on state transitions and action triplets. We define value and action-…
Learning to be safe, in finite time
Agustin Castellano, Juan Bazerque, Enrique Mallada
This paper aims to put forward the concept that learning to take safe actions in unknown environments, even with probability one guarantees, can be achieved without the need for an…