Publication

Behavioral animation of autonomous virtual agents helped by reinforcement learning

Daniel Thalmann
2003
Conference Papers

Abstract

Our research focuses on the behavioral animation of virtual humans who are capable of taking actions by themselves. We deal more specifically with reinforcement learning methodologies, which integrate in an original way the RL agent and the autonomous virtual agent in a virtual environment. With the help of a virtual environment in the form of a town, we demonstrate that it is indeed the learning process and not the optimization of RL, which is used by the AVAs

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Daniel Thalmann
2003
Conference Papers

Abstract

Official source

Related concepts (31)

Reinforcement learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.

Learning

Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single event (e.g. being burned by a hot stove), but much skill and knowledge accumulate from repeated experiences. The changes induced by learning often last a lifetime, and it is hard to distinguish learned material that seems to be "lost" from that which cannot be retrieved.

Deep reinforcement learning

Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g.

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Behavioral animation of autonomous virtual agents helped by reinforcement learning

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