Active Exploration in Markov Decision Processes

02/28/2019
by   Jean Tarbouriech, et al.
0

We introduce the active exploration problem in Markov decision processes (MDPs). Each state of the MDP is characterized by a random value and the learner should gather samples to estimate the mean value of each state as accurately as possible. Similarly to active exploration in multi-armed bandit (MAB), states may have different levels of noise, so that the higher the noise, the more samples are needed. As the noise level is initially unknown, we need to trade off the exploration of the environment to estimate the noise and the exploitation of these estimates to compute a policy maximizing the accuracy of the mean predictions. We introduce a novel learning algorithm to solve this problem showing that active exploration in MDPs may be significantly more difficult than in MAB. We also derive a heuristic procedure to mitigate the negative effect of slowly mixing policies. Finally, we validate our findings on simple numerical simulations.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/11/2018

Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes

We introduce and analyse two algorithms for exploration-exploitation in ...
research
07/06/2018

Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes

While designing the state space of an MDP, it is common to include state...
research
09/05/2019

√(n)-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank

In this paper, we consider the problem of online learning of Markov deci...
research
11/29/2019

Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation

This paper proposes a formal approach to learning and planning for agent...
research
03/06/2020

Active Model Estimation in Markov Decision Processes

We study the problem of efficient exploration in order to learn an accur...
research
05/18/2021

Learning to Act Safely with Limited Exposure and Almost Sure Certainty

This paper aims to put forward the concept that learning to take safe ac...
research
10/26/2020

Expert Selection in High-Dimensional Markov Decision Processes

In this work we present a multi-armed bandit framework for online expert...

Please sign up or login with your details

Forgot password? Click here to reset