1 research outputs found
Planning in Dynamic Environments with Conditional Autoregressive Models
We demonstrate the use of conditional autoregressive generative models (van
den Oord et al., 2016a) over a discrete latent space (van den Oord et al.,
2017b) for forward planning with MCTS. In order to test this method, we
introduce a new environment featuring varying difficulty levels, along with
moving goals and obstacles. The combination of high-quality frame generation
and classical planning approaches nearly matches true environment performance
for our task, demonstrating the usefulness of this method for model-based
planning in dynamic environments.Comment: 6 pages, 1 figure, in Proceedings of the Prediction and Generative
Modeling in Reinforcement Learning Workshop at the International Conference
on Machine Learning (ICML) in 201