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[HTML][HTML] Risk-aware shielding of partially observable monte carlo planning policies
Abstract Partially Observable Monte Carlo Planning (POMCP) is a powerful online algorithm
that can generate approximate policies for large Partially Observable Markov Decision …
that can generate approximate policies for large Partially Observable Markov Decision …
Partially Observable Monte Carlo Planning with state variable constraints for mobile robot navigation
Autonomous mobile robots employed in industrial applications often operate in complex and
uncertain environments. In this paper we propose an approach based on an extension of …
uncertain environments. In this paper we propose an approach based on an extension of …
Centralizing state-values in dueling networks for multi-robot reinforcement learning mapless navigation
We study the problem of multi-robot mapless navigation in the popular Centralized Training
and Decentralized Execution (CTDE) paradigm. This problem is challenging when each …
and Decentralized Execution (CTDE) paradigm. This problem is challenging when each …
Rule-based shielding for partially observable Monte-Carlo planning
Abstract Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm
able to generate approximate policies for large Partially Observable Markov Decision …
able to generate approximate policies for large Partially Observable Markov Decision …
Learning state-variable relationships for improving POMCP performance
We address the problem of learning state-variable relationships across different episodes in
Partially Observable Markov Decision Processes (POMDPs) to improve planning …
Partially Observable Markov Decision Processes (POMDPs) to improve planning …
Identification of unexpected decisions in partially observable monte-carlo planning: A rule-based approach
Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm able to
generate approximate policies for large Partially Observable Markov Decision Processes …
generate approximate policies for large Partially Observable Markov Decision Processes …
Learning state-variable relationships in POMCP: A framework for mobile robots
We address the problem of learning relationships on state variables in Partially Observable
Markov Decision Processes (POMDPs) to improve planning performance. Specifically, we …
Markov Decision Processes (POMDPs) to improve planning performance. Specifically, we …
[PDF][PDF] Policy Interpretation for Partially Observable Monte-Carlo Planning: a Rule-based Approach.
Abstract Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm
that can generate online policies for large Partially Observable Markov Decision Processes …
that can generate online policies for large Partially Observable Markov Decision Processes …
Active generation of logical rules for POMCP shielding
We consider the popular Partially Observable Monte-Carlo Plan-ning (POMCP) algorithm
and propose a methodology, called Active XPOMCP, for generating compact logical rules …
and propose a methodology, called Active XPOMCP, for generating compact logical rules …
Bayes-Optimal, Robust, and Distributionally Robust Policies for Uncertain MDPs
We explore the performance of different policy-making strategies within the framework of
uncertain Markov decision processes (uMDPs). In an uMDP, the agent interacts with an …
uncertain Markov decision processes (uMDPs). In an uMDP, the agent interacts with an …