Two-Armed Restless Bandits with Imperfect Information: Stochastic Control and Indexability
We present a two-armed bandit model of decision making under uncertainty where the expected return to investing in the “risky arm” increases when choosing that arm and decreases when choosing the “safe” arm.
Roland Fryer, Philipp Harms
core +1 more source
Experimental evolutionary simulations of learning, memory and life history. [PDF]
Morgan TJH, Suchow JW, Griffiths TL.
europepmc +1 more source
Low-Complexity Algorithm for Restless Bandits with Imperfect Observations
We consider a class of restless bandit problems that finds a broad application area in reinforcement learning and stochastic optimization. We consider $N$ independent discrete-time Markov processes, each of which had two possible states: 1 and 0 (`good ...
Zhang, Chengzhong +2 more
core
Model-based exploration is measurable across tasks but not linked to personality and psychiatric assessments. [PDF]
Witte K, Thalmann M, Schulz E.
europepmc +1 more source
Reliability of Decision-Making and Reinforcement Learning Computational Parameters
Mkrtchian A, Valton V, Roiser JP.
europepmc +1 more source
INDEXABILITY AND OPTIMAL INDEX POLICIES FOR A CLASS OF REINITIALISING RESTLESS BANDITS. [PDF]
Villar SS.
europepmc +1 more source
A foundation model to predict and capture human cognition. [PDF]
Binz M +39 more
europepmc +1 more source
Covariate-adjusted response-adaptive randomization for multi-arm clinical trials using a modified forward looking Gittins index rule. [PDF]
Villar SS, Rosenberger WF.
europepmc +1 more source
Anhedonic Traits Do Not Impair Performance in a 3-Arm Bandit Task. [PDF]
Ramaswamy A +4 more
europepmc +1 more source
Optimal Best Arm Identification with Fixed Confidence in Restless Bandits
We study best arm identification in a restless multi-armed bandit setting with finitely many arms. The discrete-time data generated by each arm forms a homogeneous Markov chain taking values in a common, finite state space.
Karthik, P. N. +3 more
core

