27,618 research outputs found
Memory Augmented Control Networks
Planning problems in partially observable environments cannot be solved
directly with convolutional networks and require some form of memory. But, even
memory networks with sophisticated addressing schemes are unable to learn
intelligent reasoning satisfactorily due to the complexity of simultaneously
learning to access memory and plan. To mitigate these challenges we introduce
the Memory Augmented Control Network (MACN). The proposed network architecture
consists of three main parts. The first part uses convolutions to extract
features and the second part uses a neural network-based planning module to
pre-plan in the environment. The third part uses a network controller that
learns to store those specific instances of past information that are necessary
for planning. The performance of the network is evaluated in discrete grid
world environments for path planning in the presence of simple and complex
obstacles. We show that our network learns to plan and can generalize to new
environments
Value Propagation Networks
We present Value Propagation (VProp), a set of parameter-efficient
differentiable planning modules built on Value Iteration which can successfully
be trained using reinforcement learning to solve unseen tasks, has the
capability to generalize to larger map sizes, and can learn to navigate in
dynamic environments. We show that the modules enable learning to plan when the
environment also includes stochastic elements, providing a cost-efficient
learning system to build low-level size-invariant planners for a variety of
interactive navigation problems. We evaluate on static and dynamic
configurations of MazeBase grid-worlds, with randomly generated environments of
several different sizes, and on a StarCraft navigation scenario, with more
complex dynamics, and pixels as input.Comment: Updated to match ICLR 2019 OpenReview's versio
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Writing space, living space: time, agency and place relations in Herodotusâs Histories
This chapter examines lived space in Herodotusâs Historiesâ and explores how the picture that emerges differs from abstract depictions of space. Such overly schematic representations we see articulated by the Persians at the very beginning of the Histories, or explicitly challenged by Herodotus when he âlaughs atâ the maps produced by his Ionian contemporaries that similarly divide the world into two regions of equal size (4.36.2), or more subtly undercut when Aristagoras turns up with just such a map and puts it to service an argument in favour of conquest. In particular, we want to challenge conventional readings of a polarised world of East versus West, which, while grounded in Herodotusâs concern to show how âGreeks and barbarians came into conflict with each otherâ (1.1), fail to take into account either Herodotusâs implicit rejection of the Persian model of an Asia-Europe divide in favour of an inquiry that recognises that places change over time, or the extent to which Herodotus or his historical agents relate those places to each other. Using key features of lived spaceâtime, agency and relationâ, we sketch out the beginnings of a network analysis of book 5, backed up by a close textual study of the bookâs opening episode. Both methods help to unpack the idea of the Historiesâ lived space that underpins and greatly complicates the historical agentsâ own understanding of the world around them
Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control
Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ânaturalâ) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control
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