TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: Recent years have witnessed increasing interest in the potential benefits of ‘intelligent’ autonomous machines such as robots. Honda’s Asimo humanoid robot, iRobot’s Roomba robot vacuum cleaner and Google’s driverless cars have fired the imagination of the general public, and social media buzz with speculation about a utopian world of helpful robot assistants or the coming robot apocalypse! However, there is a long way to go before autonomous systems reach the level of capabilities required for even the simplest of tasks involving human-robot interaction especially if it involves communicative behaviour such as speech and language. Of course the field of Artificial Intelligence (AI) has made great strides in these areas, and has moved on from abstract high-level rule-based paradigms to embodied architectures whose operations are grounded in real physical environments. What is still missing, however, is an overarching theory of intelligent communicative behaviour that informs system-level design decisions in order to provide a more coherent approach to system integration. This chapter introduces the beginnings of such a framework inspired by the principles of Perceptual Control Theory (PCT). In particular, it is observed that PCT has hitherto tended to view perceptual processes as a relatively straightforward series of transformations from sensation to perception, and has overlooked the potential of powerful generative model-based solutions that have emerged in practical fields such as visual or auditory scene analysis. Starting from first principles, a sequence of arguments is presented which not only shows how these ideas might be integrated into PCT, but which also extend PCT towards a remarkably symmetric architecture for a needs-driven communicative agent. It is concluded that, if behaviour is the control of perception (the central tenet of PCT), then perception (at least for communicative agents) is the simulation of behaviour.

SOLUTION: PCT and Beyond: Towards a Computational Framework for ‘Intelligent’ Communicative Systems

PROBLEM: We envision a machine learning service provider facing a continuous stream of problems with the same input domain, but with output domains that may differ. Clients present the provider with problems implicitly, by labeling a few example inputs, and then ask the provider to train models which reasonably extend their labelings to novel inputs. The provider wants to avoid constraining its users to a set of common labels, so it does not assume any particular correspondence between labels for a new task and labels for previously encountered tasks. To perform well in this setting, the provider needs a representation of the input domain which, in expectation, permits effective models for new problems to be learned efficiently from a small number of examples. While this bears a resemblance to settings considered in previous work on multitask and lifelong learning, our non-assumption of inter-task label correspondence leads to a novel algorithm: Lifelong Learner of Discriminative Representations (LLDR), which explicitly minimizes a proxy for the intra-task small-sample generalization error. We examine the relative benefits of our approach on a diverse set of real-world datasets in three significant scenarios: representation learning, multitask learning and lifelong learning.

SOLUTION: Lifelong Learning of Discriminative Representations

PROBLEM: Deep reinforcement learning (RL) has achieved several high profile successes in difficult control problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages this data to massively accelerate the learning process even from relatively small amounts of demonstration data. DQfD works by combining temporal difference updates with large-margin classification of the demonstrator’s actions. We show that DQfD has better initial performance than Deep Q-Networks (DQN) on 40 of 42 Atari games and it receives more average rewards than DQN on 27 of 42 Atari games. We also demonstrate that DQfD learns faster than DQN even when given poor demonstration data.

SOLUTION:
Learning from Demonstrations for Real World Reinforcement Learning