Arya Irani

AAMAS Conference 2010 Conference Paper

Using Training Regimens to Teach Expanding Function Approximators

• Peng Zang
• Arya Irani
• Peng Zhou
• Andrea Thomaz
• Charles Isbell

In complex real-world environments, traditional (tabular)techniques for solving Reinforcement Learning (RL) do notscale. Function approximation is needed, but unfortunately, existing approaches generally have poor convergence and optimality guarantees. Additionally, for the case of humanenvironments, it is valuable to be able to leverage humaninput. In this paper we introduce Expanding Value Function Approximation (EVFA), a function approximation algorithm that returns the optimal value function given sufficient rounds. To leverage human input, we introduce a newhuman-agent interaction scheme, training regimens, whichallow humans to interact with and improve agent learning inthe setting of a machine learning game. In experiments, weshow EVFA compares favorably to standard value approximation approaches. We also show that training regimensenable humans to further improve EVFA performance. Inour user study, we find that non-experts are able to provideeffective regimens and that they found the game fun.

IJCAI Conference 2007 Conference Paper

• Manu Sharma
• Michael Holmes
• Juan Santamaria
• Arya Irani
• Charles Isbell
• Ashwin Ram

The goal of transfer learning is to use the knowledge acquired in a set of source tasks to improve performance in a related but previously unseen target task. In this paper, we present a multilayered architecture named CAse-Based Reinforcement Learner (CARL). It uses a novel combination of Case-Based Reasoning (CBR) and Reinforcement Learning (RL) to achieve transfer while playing against the Game AI across a variety of scenarios in MadRTS(TM), a commercial Real Time Strategy game. Our experiments demonstrate that CARL not only performs well on individual tasks but also exhibits significant performance gains when allowed to transfer knowledge from previous tasks.

ICRA Conference 2005 Conference Paper

Physical Path Planning Using the GNATs

• Keith J. O'Hara
• Victor Bigio
• Eric R. Dodson
• Arya Irani
• Daniel Walker
• Tucker R. Balch

We continue our investigation into the application of pervasive, embedded networks to support multi-robot tasks. In this work we use a new a hardware platform, the GNATs, to aid in path planning. We have implemented a physical path planning algorithm on the GNATs previously studied in simulation. A distributed version of the wavefront path planning algorithm is used to propagate paths throughout the network, thereby planning a path in the real world. This creates a graph of traversable paths that are nearly optimal in a dynamic environment.