1 code implementation • ICML 2018 • Erik Talvitie
Empirically, this approach to reward learning can yield dramatic improvements in control performance when the dynamics model is flawed.
Model-based Reinforcement Learning Reinforcement Learning (RL)
7 code implementations • 18 Sep 2017 • Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, Michael Bowling
The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games.
1 code implementation • 19 Dec 2016 • Erik Talvitie
When an agent cannot represent a perfectly accurate model of its environment's dynamics, model-based reinforcement learning (MBRL) can fail catastrophically.
Model-based Reinforcement Learning reinforcement-learning +1
1 code implementation • 4 Dec 2015 • Yitao Liang, Marlos C. Machado, Erik Talvitie, Michael Bowling
The recently introduced Deep Q-Networks (DQN) algorithm has gained attention as one of the first successful combinations of deep neural networks and reinforcement learning.
no code implementations • 16 Jan 2014 • Erik Talvitie, Satinder Singh
We formalize the problem of learning a prediction profile model as a transformation of the original model-learning problem, and show empirically that one can learn prediction profile models that make a small set of important predictions even in systems that are too complex for standard generative models.
no code implementations • NeurIPS 2012 • Erik Talvitie
This paper introduces timeline trees, which are partial models of partially observable environments.
no code implementations • NeurIPS 2008 • Erik Talvitie, Satinder P. Singh
We present a novel mathematical formalism for the idea of a local model,'' a model of a potentially complex dynamical system that makes only certain predictions in only certain situations.