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Found 15 papers, 0 papers with code
A Concentration Bound for TD(0) with Function Approximation
We derive a concentration bound of the type `for all $n \geq n_0$ for some $n_0$' for TD(0) with linear function approximation.
Approximation of Convex Envelope Using Reinforcement Learning
Oberman gave a stochastic control formulation of the problem of estimating the convex envelope of a non-convex function.
Decentralised Q-Learning for Multi-Agent Markov Decision Processes with a Satisfiability Criterion
In this paper, we propose a reinforcement learning algorithm to solve a multi-agent Markov decision process (MMDP).
Actor-Critic or Critic-Actor? A Tale of Two Time Scales
We revisit the standard formulation of tabular actor-critic algorithm as a two time-scale stochastic approximation with value function computed on a faster time-scale and policy computed on a slower time-scale.
A Concentration Bound for LSPE($λ$)
The popular LSPE($\lambda$) algorithm for policy evaluation is revisited to derive a concentration bound that gives high probability performance guarantees from some time on.
Concentration of Contractive Stochastic Approximation and Reinforcement Learning
Using a martingale concentration inequality, concentration bounds `from time $n_0$ on' are derived for stochastic approximation algorithms with contractive maps and both martingale difference and Markov noises.
Prospect-theoretic Q-learning
We consider a prospect theoretic version of the classical Q-learning algorithm for discounted reward Markov decision processes, wherein the controller perceives a distorted and noisy future reward, modeled by a nonlinearity that accentuates gains and underrepresents losses relative to a reference point.
Dynamic social learning under graph constraints
We introduce a model of graph-constrained dynamic choice with reinforcement modeled by positively $\alpha$-homogeneous rewards.
Whittle index based Q-learning for restless bandits with average reward
A novel reinforcement learning algorithm is introduced for multiarmed restless bandits with average reward, using the paradigms of Q-learning and Whittle index.
Scheduling in Wireless Networks with Spatial Reuse of Spectrum as Restless Bandits
We study the problem of scheduling packet transmissions with the aim of minimizing the energy consumption and data transmission delay of users in a wireless network in which spatial reuse of spectrum is employed.
Randomized Kaczmarz for Rank Aggregation from Pairwise Comparisons
We revisit the problem of inferring the overall ranking among entities in the framework of Bradley-Terry-Luce (BTL) model, based on available empirical data on pairwise preferences.
Gradient Estimation with Simultaneous Perturbation and Compressive Sensing
This paper aims at achieving a "good" estimator for the gradient of a function on a high-dimensional space.
Empirical Q-Value Iteration
We propose a new simple and natural algorithm for learning the optimal Q-value function of a discounted-cost Markov Decision Process (MDP) when the transition kernels are unknown.
Reinforcement Learning for Matrix Computations: PageRank as an Example
In this spirit, we propose a reinforcement learning algorithm for PageRank computation that is fashioned after analogous schemes for approximate dynamic programming.
Distributed Reinforcement Learning via Gossip
We consider the classical TD(0) algorithm implemented on a network of agents wherein the agents also incorporate the updates received from neighboring agents using a gossip-like mechanism.
