Fast deep reinforcement learning using online adjustments from the past

We propose Ephemeral Value Adjusments (EVA): a means of allowing deep reinforcement learning agents to rapidly adapt to experience in their replay buffer. EVA shifts the value predicted by a neural network with an estimate of the value function found by planning over experience tuples from the replay buffer near the current state. EVA combines a number of recent ideas around combining episodic memory-like structures into reinforcement learning agents: slot-based storage, content-based retrieval, and memory-based planning. We show that EVAis performant on a demonstration task and Atari games.Comment: Accepted at NIPS 201

A graphical programming interface for a children's constructionist learning environment

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 41).by Andrew C. Cheng.M.Eng

Model-based reinforcement learning: A survey

Reinforcement learning is an important branch of machine learning and artificial intelligence. Compared with traditional reinforcement learning, model-based reinforcement learning obtains the action of the next state by the model that has been learned, and then optimizes the policy, which greatly improves data efficiency. Based on the present status of research on model-based reinforcement learning at home and abroad, this paper comprehensively reviews the key techniques of model-based reinforcement learning, summarizes the characteristics, advantages and defects of each technology, and analyzes the application of model-based reinforcement learning in games, robotics and brain science

Natural Curiosity

Curiosity is evident in humans of all sorts from early infancy, and it has also been said to appear in a wide range of other animals, including monkeys, birds, rats, and octopuses. The classical definition of curiosity as an intrinsic desire for knowledge may seem inapplicable to animal curiosity: one might wonder how and indeed whether a rat could have such a fancy desire. Even if rats must learn many things to survive, one might expect their learning must be driven by simpler incentives, such as hunger. One might also wonder what proximal signals could guide animals towards knowledge itself, or how something as abstract as knowledge could ever be a motivational target for an unreflective animal. Taking a cue from recent work in reinforcement learning, I argue that surprise functions as a reward signal for the curious animal, and then show how this amounts to a desire for knowledge gain, where knowledge is conceived of as a cognitive adaptation to reality. This adaptation results in a mental state whose existence depends essentially on the truth of its contents, a factive mental state. Curious creatures benefit from an interaction between the prediction-error correction processes of basic learning and the active surprise-seeking force of their curiosity. This internally adversarial interaction accelerates knowledge gain in ways that are very helpful for agents with the restrictions of biological creatures, in environments with the complexity of our natural world

The Chronicle [October 9, 1979]

The Chronicle, October 9, 1979https://repository.stcloudstate.edu/chron/3142/thumbnail.jp