A Computational Theory of Operant Conditioning with Application to Trainable Robots

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This award supports the development of a computationally explicit theory of operant conditioning, in which animals determine the effects of their actions and adjust their behavior to maximize reward. The Rescorla-Wagner model of classical conditioning and its various descendants have yielded considerable insight into how associations between sensory stimuli andreflex actions may be acquired. But operant conditioning evokes more complex and deliberate behavior patterns, for which there is no comparable computational model. The theory being developed includes four types of learning: (i) on-line learning of reward predictors based on observed reinforcement contingencies, (ii) acquiring secondary reinforcers, such as the sound of a food dispenser being activated, (iii) generating new actions by selecting and shaping innate behaviors, and (iv) refining perception to focus on task-relevant signal discriminations. In addition to testing the theory with computer simulations of classic animal learning experiments such as the Delayed Match to Sample task, the theory is being embodied in an RWI B21 mobile robot. This research promises a new class of learning robots that can interact with people in much the same way that animals interact with their human trainers