Results 111 to 120 of about 257 (142)

The refinement paradox and cumulative cultural evolution: Complex products of collective improvement favor conformist outcomes, blind copying, and hyper-credulity. [PDF]

PLoS Comput Biol
Miu E, Rendell L, Bowles S, Boyd R, Cownden D, Enquist M, Eriksson K, Feldman MW, Lillicrap T, McElreath R, Murray S, Ounsley J, Lala KN.   +12 more
europepmc   +1 more source

Multi-channel opportunistic access : a restless multi-armed bandit perspective

Accès opportuniste dans les systèmes de communication multi-canaux : une perspective du problème de bandit-manchot Dans cette thèse, nous abordons le problème fondamental de l'accès au spectre opportuniste dans un système de communication multi-canal.
openaire   +2 more sources

GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits

The restless multi-armed bandit (RMAB) framework is a popular model with applications across a wide variety of fields. However, its solution is hindered by the exponentially growing state space (with respect to the number of arms) and the combinatorial ...
Chen, Gongpu, Gunduz, Deniz, Liew, Soung Chang   +2 more
core  

Some of the next articles are maybe not open access.

Related searches:

On a Class of Restless Multi-armed Bandits with Deterministic Policies

2018 International Conference on Signal Processing and Communications (SPCOM), 2018
We describe and analyze a restless multi-armed bandit (RMAB) in which, in each time-slot, the instantaneous reward from the playing of an arm depends on the time since the arm was last played. This model is motivated by recommendation systems where the payoff from a recommendation on depends the recommendation history.
Prakirt Raj Jhunjhunwala, Sharayu Moharir, D Manjunath   +2 more
exaly   +2 more sources

Optimality of myopic policy for a class of monotone affine restless multi-armed bandits

2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012
We formulate a general class of restless multi-armed bandits with n independent and stochastically identical arms. Each arm is in a real-valued state s ∈ [s 0 , s max ]. Selecting an arm with state s yields an immediate reward with expectation R(s). The state of the arm that is selected stochastically jumps from its current value s to either s max or ...
Tara Javidi, Bhaskar Krishnamachari
exaly   +2 more sources

State-Aware Value Function Approximation with Attention Mechanism for Restless Multi-armed Bandits

, 2021
The restless multi-armed bandit (RMAB) problem is a generalization of the multi-armed bandit with non-stationary rewards. Its optimal solution is intractable due to exponentially large state and action spaces with respect to the number of arms.
Guangjian Tian
exaly   +2 more sources

Risk-Aware Interventions in Public Health: Planning with Restless Multi-Armed Bandits [PDF]

International Joint Conference on Autonomous Agents and Multiagent Systems, 2021
Community Health Workers (CHWs) form an important component of health-care systems globally, especially in low-resource settings. CHWs are often tasked with monitoring the health of and intervening on their patient cohort. Previous work has developed several classes of Restless Multi-Armed Bandits (RMABs) that are computationally tractable and ...
Aditya Mate, Andrew Perrault, Milind Tambe   +2 more
openaire   +1 more source

Asymptotically optimal priority policies for indexable and nonindexable restless bandits [PDF]

Annals of Applied Probability, 2016
We study the asymptotic optimal control of multi-class restless bandits. A restless bandit is a controllable stochastic process whose state evolution depends on whether or not the bandit is made active.
Verloop, Ina Maria
exaly   +2 more sources