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Found 10 papers, 4 papers with code
Combining a Meta-Policy and Monte-Carlo Planning for Scalable Type-Based Reasoning in Partially Observable Environments
The design of autonomous agents that can interact effectively with other agents without prior coordination is a core problem in multi-agent systems.
A Surprisingly Simple Continuous-Action POMDP Solver: Lazy Cross-Entropy Search Over Policy Trees
At each planning step, our method uses a novel lazy Cross-Entropy method to search the space of policy trees, which provide a simple policy representation.
Adaptive Discretization using Voronoi Trees for Continuous POMDPs
ADVT uses the estimated diameters of the cells to form an upper-confidence bound on the action value function within the cell, guiding the Monte Carlo Tree Search expansion and further discretization of the action space.
Adaptive Discretization using Voronoi Trees for Continuous-Action POMDPs
A Voronoi tree is a Binary Space Partitioning (BSP) that implicitly maintains the partition of a cell as the Voronoi diagram of two points sampled from the cell.
An NCAP-like Safety Indicator for Self-Driving Cars
This paper proposes a mechanism to assess the safety of autonomous cars.
An On-Line POMDP Solver for Continuous Observation Spaces
Most on-line solvers rely on discretising the observation space or artificially limiting the number of observations that are considered during planning to compute tractable policies.
Partially Observable Planning and Learning for Systems with Non-Uniform Dynamics
We propose a neural network architecture, called TransNet, that combines planning and model learning for solving Partially Observable Markov Decision Processes (POMDPs) with non-uniform system dynamics.
Autonomous Penetration Testing using Reinforcement Learning
Further work is needed in developing scalable RL algorithms and testing these algorithms in larger and higher fidelity environments.
A Stochastic Process Model of Classical Search
In this paper, we formalize the process of classical search as a metalevel decision problem, the Abstract Search MDP.
Analysis of Asymptotically Optimal Sampling-based Motion Planning Algorithms for Lipschitz Continuous Dynamical Systems
The purpose of this paper is to show that asymptotically optimal motion planning for dynamical systems with differential constraints can be achieved without the use of a steering function.
Robotics
