Credit assignment in multiple goal embodied visuomotor behavior

The intrinsic complexity of the brain can lead one to set aside issues related to its relationships with the body, but the field of embodied cognition emphasizes that understanding brain function at the system level requires one to address the role of the brain-body interface. It has only recently been appreciated that this interface performs huge amounts of computation that does not have to be repeated by the brain, and thus affords the brain great simplifications in its representations. In effect the brain’s abstract states can refer to coded representations of the world created by the body. But even if the brain can communicate with the world through abstractions, the severe speed limitations in its neural circuitry mean that vast amounts of indexing must be performed during development so that appropriate behavioral responses can be rapidly accessed. One way this could happen would be if the brain used a decomposition whereby behavioral primitives could be quickly accessed and combined. This realization motivates our study of independent sensorimotor task solvers, which we call modules, in directing behavior. The issue we focus on herein is how an embodied agent can learn to calibrate such individual visuomotor modules while pursuing multiple goals. The biologically plausible standard for module programming is that of reinforcement given during exploration of the environment. However this formulation contains a substantial issue when sensorimotor modules are used in combination: The credit for their overall performance must be divided amongst them. We show that this problem can be solved and that diverse task combinations are beneficial in learning and not a complication, as usually assumed. Our simulations show that fast algorithms are available that allot credit correctly and are insensitive to measurement noise

Towards Informed Exploration for Deep Reinforcement Learning

In this thesis, we discuss various techniques for improving exploration for deep reinforcement learning. We begin with a brief review of reinforcement learning (RL) and the fundamental v.s. exploitation trade-off. Then we review how deep RL has improved upon classical and summarize six categories of the latest exploration methods for deep RL, in the order increasing usage of prior information. We then explore representative works in three categories discuss their strengths and weaknesses. The first category, represented by Soft Q-learning, uses regularization to encourage exploration. The second category, represented by count-based via hashing, maps states to hash codes for counting and assigns higher exploration to less-encountered states. The third category utilizes hierarchy and is represented by modular architecture for RL agents to play StarCraft II. Finally, we conclude that exploration by prior knowledge is a promising research direction and suggest topics of potentially impact

Solving large stochastic planning problems using multiple dynamic abstractions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 165-172).One of the goals of AI is to produce a computer system that can plan and act intelligently in the real world. It is difficult to do so, in part because real-world domains are very large. Existing research generally deals with the large domain size using a static representation and exploiting a single type of domain structure. This leads either to an inability to complete planning on larger domains or to poor solution quality because pertinent information is discarded. This thesis creates a framework that encapsulates existing and new abstraction and approximation methods into modules and combines arbitrary modules into a 'hierarchy that allows for dynamic representation changes. The combination of different abstraction methods allows many qualitatively different types of structure in the domain to be exploited simultaneously. The ability to change the representation dynamically allows the framework to take advantage of how different domain subparts are relevant in different ways at different times. Since the current plan tracks the current representation, choosing to simplify (or omit) distant or improbable areas of the domain sacrifices little in the way of solution quality while making the planning problem considerably easier.(cont.) The module hierarchy approach leads to greater abstraction that is tailored to the domain and therefore need not give up hope of creating reasonable solutions. While there are no optimality guarantees, experimental results show that suitable module choices gain computational tractability at little cost to behavioral optimality and allow the module hierarchy to solve larger and more interesting domains than previously possible.by Kurt Alan Steinkraus.Ph.D