Configurable Markov Decision Processes

Abstract

In many real-world problems, there is the possibility to configure, to a limited extent, some environmental parameters to improve the performance of a learning agent. In this paper, we propose a novel framework, Configurable Markov Decision Processes (Conf-MDPs), to model this new type of interaction with the environment. Furthermore, we provide a new learning algorithm, Safe Policy-Model Iteration (SPMI), to jointly and adaptively optimize the policy and the environment configuration. After having introduced our approach and derived some theoretical results, we present the experimental evaluation in two explicative problems to show the benefits of the environment configurability on the performance of the learned policy.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keymetelli2018configurable

Related papers

• OCMDP: Observation-constrained Markov Decision Process (2024)0.00
• A Safe Exploration Approach To Constrained Markov Decision Processes (2023)0.00
• Policy Dispersion In Non-markovian Environment (2023)0.00
• Act As You Learn: Adaptive Decision-making In Non-stationary Markov Decision Processes (2024)0.00
• Intrinsically Motivated Hierarchical Policy Learning In Multi-objective Markov Decision Processes (2023)4.52
• Policy Learning For Robust Markov Decision Process With A Mismatched Generative Model (2022)0.00
• Robust Anytime Learning Of Markov Decision Processes (2022)0.00
• Safe Reinforcement Learning For Constrained Markov Decision Processes With Stochastic Stopping Time (2024)2.26