Abstract-Autonomous agents that learn from reward on highdimensional visual observations must learn to simplify the raw observations in both space (i.e., dimensionality reduction) and time (i.e., prediction), so that reinforcement learning becomes tractable and effective. Training the spatial and temporal models requires an appropriate sampling scheme, which cannot be hardcoded if the algorithm is to be general. Intrinsic rewards are associated with samples that best improve the agent's model of the world. Yet the dynamic nature of an intrinsic reward signal presents a major obstacle to successfully realizing an efficient curiosity-drive. TD-based incremental reinforcement learning approaches fail to adapt quickly enough to effectively exploit the curiosity signal. In this paper, a novel artificial curiosity system with planning is implemented, based on developmental or continual learning principles. Least-squares policy iteration is used with an agent's internal forward model, to efficiently assign values for maximizing combined external and intrinsic reward. The properties of this system are illustrated in a highdimensional, noisy, visual environment that requires the agent to explore. With no useful external value information early on, the self-generated intrinsic values lead to actions that improve both its spatial (perceptual) and temporal (cognitive) models. Curiosity also leads it to learn how it could act to maximize external reward.
Abstract. The ability to identify novel patterns in observations is an essential aspect of intelligence. In a computational framework, the notion of a pattern can be formalized as a program that uses regularities in observations to store them in a compact form, called a compressor. The search for interesting patterns can then be stated as a search to better compress the history of observations. This paper introduces coherence progress, a novel, general measure of interestingness that is independent of its use in a particular agent and the ability of the compressor to learn from observations. Coherence progress considers the increase in coherence obtained by any compressor when adding an observation to the history of observations thus far. Because of its applicability to any type of compressor, the measure allows for an easy, quick, and domain-specific implementation. We demonstrate the capability of coherence progress to satisfy the requirements for qualitatively measuring interestingness on a Wikipedia dataset.
Abstract. We present an architecture based on self-organizing maps for learning a sensory layer in a learning system. The architecture, temporal network for transitions (TNT), enjoys the freedoms of unsupervised learning, works on-line, in non-episodic environments, is computationally light, and scales well. TNT generates a predictive model of its internal representation of the world, making planning methods available for both the exploitation and exploration of the environment. Experiments demonstrate that TNT learns nice representations of classical reinforcement learning mazes of varying size (up to 20 × 20) under conditions of high-noise and stochastic actions.
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