  Model-based reinforcement learning (MBRL) methods have shown strong sample
efficiency and performance across a variety of tasks, including when faced with
high-dimensional visual observations. These methods learn to predict the
environment dynamics and expected reward from interaction and use this
predictive model to plan and perform the task. However, MBRL methods vary in
their fundamental design choices, and there is no strong consensus in the
literature on how these design decisions affect performance. In this paper, we
study a number of design decisions for the predictive model in visual MBRL
algorithms, focusing specifically on methods that use a predictive model for
planning. We find that a range of design decisions that are often considered
crucial, such as the use of latent spaces, have little effect on task
performance. A big exception to this finding is that predicting future
observations (i.e., images) leads to significant task performance improvement
compared to only predicting rewards. We also empirically find that image
prediction accuracy, somewhat surprisingly, correlates more strongly with
downstream task performance than reward prediction accuracy. We show how this
phenomenon is related to exploration and how some of the lower-scoring models
on standard benchmarks (that require exploration) will perform the same as the
best-performing models when trained on the same training data. Simultaneously,
in the absence of exploration, models that fit the data better usually perform
better on the downstream task as well, but surprisingly, these are often not
the same models that perform the best when learning and exploring from scratch.
These findings suggest that performance and exploration place important and
potentially contradictory requirements on the model.

Machine Learning