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Found 10 papers, 0 papers with code
Full Gradient Deep Reinforcement Learning for Average-Reward Criterion
We extend the provably convergent Full Gradient DQN algorithm for discounted reward Markov decision processes from Avrachenkov et al. (2021) to average reward problems.
A Concentration Bound for Distributed Stochastic Approximation
We revisit the classical model of Tsitsiklis, Bertsekas and Athans for distributed stochastic approximation with consensus.
Concentration bounds for SSP Q-learning for average cost MDPs
We derive a concentration bound for a Q-learning algorithm for average cost Markov decision processes based on an equivalent shortest path problem, and compare it numerically with the alternative scheme based on relative value iteration.
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning
In addition to standard Lipschitz assumptions and conditions on the vanishing step-size sequence, it is assumed that the associated \textit{mean flow} $ \tfrac{d}{dt} \vartheta_t = \bar{f}(\vartheta_t)$, is globally asymptotically stable with stationary point denoted $\theta^*$, where $\bar{f}(\theta)=\text{ E}[f(\theta,\Phi)]$ with $\Phi$ having the stationary distribution of the chain.
A Unified Batch Selection Policy for Active Metric Learning
To avoid redundancy between triplets, our method collectively selects batches with maximum joint entropy, which simultaneously captures both informativeness and diversity.
Online Reinforcement Learning of Optimal Threshold Policies for Markov Decision Processes
To overcome the curses of dimensionality and modeling of Dynamic Programming (DP) methods to solve Markov Decision Process (MDP) problems, Reinforcement Learning (RL) methods are adopted in practice.
Opinion shaping in social networks using reinforcement learning
One leads to a convex optimization problem and the other to a non-convex one.
A Structure-aware Online Learning Algorithm for Markov Decision Processes
In this paper, we propose a new RL algorithm which utilizes the known threshold structure of the optimal policy while learning by reducing the feasible policy space.
Parallel and Distributed Approaches for Graph Based Semi-supervised Learning
It is based onsampling of nodes by performing a random walk on the graph.
Approachability in Stackelberg Stochastic Games with Vector Costs
Firstly, we give a simple and computationally tractable strategy for approachability for Stackelberg stochastic games along the lines of Blackwell's.
