A Computational Theory of Operant Conditioning with Application to Trainable Robots

该奖项支持操作性条件反射的计算显式理论的发展，其中动物确定其行为的影响并调整其行为以最大化奖励。 经典条件反射的Rescorla-Wagner模型和它的各种后代已经对感觉刺激和反射行为之间的联系是如何获得的产生了相当多的见解。 但操作性条件反射唤起了更复杂和更刻意的行为模式，对此没有可比的计算模型。 正在开发的理论包括四种类型的学习：（i）基于观察到的强化偶然性在线学习奖励预测器，（ii）获取次级奖励，例如食物分配器被激活的声音，（iii）通过选择和塑造先天行为来产生新的行动，以及（iv）改进感知以专注于任务相关的信号辨别。 除了用计算机模拟经典动物学习实验（如延迟匹配样本任务）来测试该理论外，该理论还体现在RWI B21移动的机器人中。 这项研究预示着一种新的学习机器人，它可以与人类互动，就像动物与人类训练员互动一样。

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