Navigation with memory in a partially observable environment

2006 ◽  
Vol 54 (1) ◽  
pp. 84-94 ◽  
Author(s):  
A. Montesanto ◽  
G. Tascini ◽  
P. Puliti ◽  
P. Baldassarri
Keyword(s):  
Partially Observable ◽  
With Memory

Emergent Tangled Program Graphs in Partially Observable Recursive Forecasting and ViZDoom Navigation Tasks

2021 ◽  
Vol 1 (3) ◽  
pp. 1-41
Author(s):  
Stephen Kelly ◽  
Robert J. Smith ◽  
Malcolm I. Heywood ◽  
Wolfgang Banzhaf
Keyword(s):  
Empirical Evaluation ◽  
Complete Information ◽  
Visual Navigation ◽  
Sources Of Information ◽  
Variation Operators ◽  
Incremental Construction ◽  
Partially Observable ◽  
First Person Shooter ◽  
With Memory ◽  
Program Graph

Modularity represents a recurring theme in the attempt to scale evolution to the design of complex systems. However, modularity rarely forms the central theme of an artificial approach to evolution. In this work, we report on progress with the recently proposed Tangled Program Graph (TPG) framework in which programs are modules. The combination of the TPG representation and its variation operators enable both teams of programs and graphs of teams of programs to appear in an emergent process. The original development of TPG was limited to tasks with, for the most part, complete information. This work details two recent approaches for scaling TPG to tasks that are dominated by partially observable sources of information using different formulations of indexed memory. One formulation emphasizes the incremental construction of memory, again as an emergent process, resulting in a distributed view of state. The second formulation assumes a single global instance of memory and develops it as a communication medium, thus a single global view of state. The resulting empirical evaluation demonstrates that TPG equipped with memory is able to solve multi-task recursive time-series forecasting problems and visual navigation tasks expressed in two levels of a commercial first-person shooter environment.

scholarly journals Verifiable RNN-Based Policies for POMDPs Under Temporal Logic Constraints

2020 ◽  
Author(s):  
Steven Carr ◽  
Nils Jansen ◽  
Ufuk Topcu
Keyword(s):  
Synthesis Methods ◽  
Sequential Decision ◽  
Major Drawback ◽  
State Controller ◽  
Finite State ◽  
Markov Decision ◽  
Verification Tools ◽  
Partially Observable ◽  
With Memory ◽  
Effective Representation

Recurrent neural networks (RNNs) have emerged as an effective representation of control policies in sequential decision-making problems. However, a major drawback in the application of RNN-based policies is the difficulty in providing formal guarantees on the satisfaction of behavioral specifications, e.g. safety and/or reachability. By integrating techniques from formal methods and machine learning, we propose an approach to automatically extract a finite-state controller (FSC) from an RNN, which, when composed with a finite-state system model, is amenable to existing formal verification tools. Specifically, we introduce an iterative modification to the so-called quantized bottleneck insertion technique to create an FSC as a randomized policy with memory. For the cases in which the resulting FSC fails to satisfy the specification, verification generates diagnostic information. We utilize this information to either adjust the amount of memory in the extracted FSC or perform focused retraining of the RNN. While generally applicable, we detail the resulting iterative procedure in the context of policy synthesis for partially observable Markov decision processes (POMDPs), which is known to be notoriously hard. The numerical experiments show that the proposed approach outperforms traditional POMDP synthesis methods by 3 orders of magnitude within 2% of optimal benchmark values.

Capito-Lunate Fusion with Memory Staples

2015 ◽  
Vol 04 (S 01) ◽  
Author(s):  
M. Solomons
Keyword(s):  
With Memory

Design of Predistorter with Efficient Updating Algorithm of Power Amplifier with Memory Effect

2012 ◽  
Vol E95-C (3) ◽  
pp. 382-394
Author(s):  
Yasuyuki OISHI ◽  
Shigekazu KIMURA ◽  
Eisuke FUKUDA ◽  
Takeshi TAKANO ◽  
Daisuke TAKAGO ◽  
...  
Keyword(s):  
Memory Effect ◽  
Power Amplifier ◽  
With Memory

Improved Post-Copy Live Migration with Memory Page Prefetching

2020 ◽  
Vol 16 (5) ◽  
pp. 728
Author(s):  
Cui Yong ◽  
Chen Haoran ◽  
Zhu Liang
Keyword(s):  
Live Migration ◽  
With Memory

Transition (Butler Dismissed)

2017 ◽  
Author(s):  
Galen Strawson
Keyword(s):  
Personal Identity ◽  
Temporal Continuity ◽  
With Memory

This chapter examines John Locke's theory of personal identity, which he has defined in terms of the reach of consciousness in beings who qualify as persons (being in particular fully self-conscious, able to think of past and future, and “capable of a law”). It starts with the notion that a person is an object of a certain sort, and must exemplify a certain sort of temporal continuity, if it is to continue to exist. Locke assumes that any candidate person has such continuity. The chapter also considers which parts of a subject of experience's continuous past are features or aspects or parts of the person that it now is before concluding with an analysis of Joseph Butler's incorrect identification of consciousness with memory in his objection to Locke's argument that a person can survive a change in its thinking substance even if its thinking substance is immaterial.

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