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- Vijaykumar Gullapalli
- Neural Networks
- 1990

- Vijaykumar Gullapalli, Roderic A. Grupen, +11 authors Judy Franklin
- 1992

REINFORCEMENT LEARNING AND ITS APPLICATION TO CONTROL

- Vijaykumar Gullapalli, Andrew G. Barto
- NIPS
- 1993

Reinforcement Learning methods based on approximating dynamic programming (DP) are receiving increased attention due to their utility in forming reactive control policies for systems embedded in dynamic environments. Environments are usually modeled as controlled Markov processes, but when the environment model is not known a priori, adaptive methods are… (More)

- Vijaykumar Gullapalli
- Neurocomputing
- 1995

In this paper, a peg-in-hole insertion task is used as an example to illustrate the utility of direct as-sociative reinforcement learning methods for learning control under real-world conditions of uncertainty and noise. An associative reinforcement learning system has to learn appropriate actions in various situations through search guided by evaluative… (More)

The \forward modeling" approach of Jor-dan and Rumelhart has been shown to be applicable when supervised learning methods are to be used for solving reinforcement learning tasks. Because such tasks are natural candidates for the application of reinforcement learning methods, there is a need to evaluate the relative merits of these two learning methods on… (More)

- Vijaykumar Gullapalli
- NIPS
- 1992

A peg-in-hole insertion task is used as an example to illustrate the utility of direct associative reinforcement learning methods for learning control under real-world conditions of uncertainty and noise. Task complexity due to the use of an unchamfered hole and a clearance of less than 0:2mm is compounded by the presence of positional uncertainty of… (More)

be facilitated by rst learning to solve related simpler problems. The term \shaping" itself has been attributed to the psychologist Skinner 7], who used the technique to train animals such as rats and pigeons to perform complicated sequences of actions for rewards. Skinner describes how the technique is used to train pigeons to peck at a speciic spot: We… (More)

|Associative reinforcement learning (ARL) tasks de ned originally by Barto and Anandan [1] combine elements of problems involving optimization under uncertainty, studied by learning automata theorists, and supervised learning pattern-classi cation. The stochastic real-valued (SRV) unit algorithm [6] has been designed for an extended version of ARL tasks… (More)

- Vijaykumar Gullapalli
- Robotics and Autonomous Systems
- 1995