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Found 10 papers, 1 papers with code
Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models
Second, we must bridge the gap between the more abstract level at which we understand what a skill does and the low-level state variables used within its code.
Learning and Solving Regular Decision Processes
Regular Decision Processes (RDPs) are a recently introduced model that extends MDPs with non-Markovian dynamics and rewards.
Reinforcement Learning with Non-Markovian Rewards
Past work considered the problem of modeling and solving MDPs with non-Markovian rewards (NMR), but we know of no principled approaches for RL with NMR.
Privacy Preserving Multi-Agent Planning with Provable Guarantees
In privacy-preserving multi-agent planning, a group of agents attempt to cooperatively solve a multi-agent planning problem while maintaining private their data and actions.
A Planning Approach to Monitoring Behavior of Computer Programs
Given a system call trace, we simulate the corresponding operators on our model and by observing the properties of the state reached, we learn about the nature of the original program and its behavior.
Replanning in Domains with Partial Information and Sensing Actions
The state of the classical planning problem generated in this approach captures the belief state of the agent in the original problem.
A Heuristic Search Approach to Planning with Continuous Resources in Stochastic Domains
We consider the problem of optimal planning in stochastic domains with resource constraints, where the resources are continuous and the choice of action at each step depends on resource availability.
Generic Preferences over Subsets of Structured Objects
For example, preferences over the content of an online newspaper should have this form: At each viewing, the newspaper contains a subset of the set of articles currently available.
Structured Reachability Analysis for Markov Decision Processes
Another contribution is the illustration of how the compact representation of reachability constraints can be exploited by several existing (exact and approximate) abstraction algorithms for MDPs.
An MDP-based Recommender System
We argue that it is more appropriate to view the problem of generating recommendations as a sequential decision problem and, consequently, that Markov decision processes (MDP) provide a more appropriate model for Recommender systems.
