Wednesday, October 26, 2016

Spatio-Temporal Data from Reinforcement Learning

Applying RL algorithms, in a spatial POMDP domains produces spatio-temporal data that it is necessary to analyse and organise in order to produce effective control policies.

There has recently been a great amount of progress in analysing cortical representations of space and time in terms of place-cells, gird cells.  This work has the potential to inform the area of RL in terms of efficient encoding and reuse of spatial data.
The overlap between RL and the neuroscience of mapping local space is particularly interesting as RL can produce raw spatio-temporal data from local sensors.  This provides us with an opportunity to analyse, explore and identify the computational and behavioural principles that enable efficient learning of spatial behaviours.

Neuroscience - Mapping local space

A great introduction to this work is available through the lectures from three Nobel price winners in this area:
There is also a TED talk from 2011 on this subject by Neil Burgess from UCL (in O'Keefe's group) entitled How your brain tells you where you are.  Burgess has a range of more general papers on spatial cognition, including:

A brief colloquial presentation of this research entitled 'Discovering grid cells' is available from the Kavli Insitute of Systems Neuroscience's Centre for Neural Computation.

There was also a nice review article from in the Annual Review of Neuroscience entitled 'Place Cells, Grid Cells, and the Brain's Spatial Representation System', Vol. 31:69-89, 2008, by Edvard I. Moser, Emilio Kropff and May-Britt Moser.

There was also a Hippocampus special issue in grid-cells in 2008 edited by Michael E. Hasselmo, Edvard I. Moser and May-Britt Moser.

Recently there was another summary article in Nature Reviews Neuroscience entitled 'Grid cells and cortical representation', Vol. 15:466–481, 2014, by Edvard I. Moser, Yasser Roudi, Menno P. Witter, Clifford Kentros, Tobias Bonhoeffer and May-Britt Moser.

Further relevant work has recently been presented in an article entitled 'Grid Cells and Place Cells: An Integrated View of their Navigational and Memory Function' in Trends in Neurosciences, Vol. 38(12):763–775, 2015, by Honi Sanders, César Rennó-Costa, Marco Idiart and John Lisman.

A more general introcution to

Computational Approaches

There is a review article on computational approaches to these issues entitled 'Place Cells, Grid Cells, Attractors, and Remapping' in Neural Plasticity, Vol. 2011, 2011 by Kathryn J. Jeffery.

Other relevant articles:

  • 'Impact of temporal coding of presynaptic entorhinal cortex grid cells on the formation of hippocampal place fields' in Neural Networks, 21(2-3):303-310, 2008, by  Colin Molter and Yoko Yamaguchi.
  • 'An integrated model of autonomous topological spatial cognition' in Autonomous Robots, 40(8):1379–1402, 2016, by Hakan Karaoğuz and Işıl Bozma.
  • In 2003, in a paper entitled 'Subsymbolic action planning for mobile robots: Do plans need to be precise?', John Pisokas and Ulrich Nehmzow used the topology-preserving properties of self-organising maps to create spatial proto-maps that supported sub-symbolic action planning in a mobile robot.
  • A paper entitled Emergence of multimodal action representations from neural network self-organization by German I. Parisi, Jun Tani, Cornelius Weber and Stefan Wermter includes an intteresting section called 'A self-organizing spatiotemporal hierarchy' wich addresses the automated structuring of spetio-temporal data.