Oversampled and undersolved: Depressive rumination from an active inference perspective
Rumination is a widely recognized cognitive deviation in depression. Despite the
recognition, researchers have struggled to explain why patients cannot disengage from the …
recognition, researchers have struggled to explain why patients cannot disengage from the …
Learning stochastic shortest path with linear function approximation
We study the stochastic shortest path (SSP) problem in reinforcement learning with linear
function approximation, where the transition kernel is represented as a linear mixture of …
function approximation, where the transition kernel is represented as a linear mixture of …
Stochastic shortest path: Minimax, parameter-free and towards horizon-free regret
We study the problem of learning in the stochastic shortest path (SSP) setting, where an
agent seeks to minimize the expected cost accumulated before reaching a goal state. We …
agent seeks to minimize the expected cost accumulated before reaching a goal state. We …
Implicit finite-horizon approximation and efficient optimal algorithms for stochastic shortest path
L Chen, M Jafarnia-Jahromi… - Advances in Neural …, 2021 - proceedings.neurips.cc
We introduce a generic template for developing regret minimization algorithms in the
Stochastic Shortest Path (SSP) model, which achieves minimax optimal regret as long as …
Stochastic Shortest Path (SSP) model, which achieves minimax optimal regret as long as …
Policy optimization with linear temporal logic constraints
We study the problem of policy optimization (PO) with linear temporal logic (LTL) constraints.
The language of LTL allows flexible description of tasks that may be unnatural to encode as …
The language of LTL allows flexible description of tasks that may be unnatural to encode as …
Online learning for stochastic shortest path model via posterior sampling
We consider the problem of online reinforcement learning for the Stochastic Shortest Path
(SSP) problem modeled as an unknown MDP with an absorbing state. We propose PSRL …
(SSP) problem modeled as an unknown MDP with an absorbing state. We propose PSRL …
Posterior sampling-based online learning for the stochastic shortest path model
We consider the problem of online reinforcement learning for the Stochastic Shortest Path
(SSP) problem modeled as an unknown MDP with an absorbing state. We propose PSRL …
(SSP) problem modeled as an unknown MDP with an absorbing state. We propose PSRL …
A provably efficient sample collection strategy for reinforcement learning
One of the challenges in online reinforcement learning (RL) is that the agent needs to trade
off the exploration of the environment and the exploitation of the samples to optimize its …
off the exploration of the environment and the exploitation of the samples to optimize its …
Identification of blackwell optimal policies for deterministic MDPs
V Boone, B Gaujal - International Conference on Artificial …, 2023 - proceedings.mlr.press
This paper investigates a new learning problem, the identification of Blackwell optimal
policies on deterministic MDPs (DMDPs): A learner has to return a Blackwell optimal policy …
policies on deterministic MDPs (DMDPs): A learner has to return a Blackwell optimal policy …
Reaching goals is hard: Settling the sample complexity of the stochastic shortest path
We study the sample complexity of learning an $\epsilon $-optimal policy in the Stochastic
Shortest Path (SSP) problem. We first derive sample complexity bounds when the learner …
Shortest Path (SSP) problem. We first derive sample complexity bounds when the learner …