Artificial Intelligence
Statistically efficient estimation using population coding
Neural Computation
A reinforcement learning model of selective visual attention
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Categorisation through evidence accumulation in an active vision system
Connection Science
A model of reaching that integrates reinforcement learning and population encoding of postures
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
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One of the main claims of the active vision framework is that finding data on the basis of task requirements is more efficient than reconstructing the whole scene by performing a complete visual scan. To be successful, this approach requires that agents learn visual routines to direct overt attention to locations with the information needed to accomplish the task. In ecological conditions, learning such visual routines is difficult due to the partial observability of the world, the changes in the environment, and the fact that learning signals might be indirect. This paper uses a reinforcement-learning actor-critic model to study how visual routines can be formed, and then adapted when the environment changes, in a system endowed with a controllable gaze and reaching capabilities. The tests of the model show that: (a) the autonomously-developed visual routines are strongly dependent on the task and the statistical properties of the environment; (b) when the statistics of the environment change, the performance of the system remains rather stable thanks to the re-use of previously discovered visual routines while the visual exploration policy remains for long time sub-optimal. We conclude that the model has a robust behaviour but the acquisition of an optimal visual exploration policy is particularly hard given its complex dependence on statistical properties of the environment, showing another of the difficulties that adaptive active vision agents must face.