  A common approach to solving physical-reasoning tasks is to train a value
learner on example tasks. A limitation of such an approach is it requires
learning about object dynamics solely from reward values assigned to the final
state of a rollout of the environment. This study aims to address this
limitation by augmenting the reward value with additional supervisory signals
about object dynamics. Specifically,we define a distance measure between the
trajectory of two target objects, and use this distance measure to characterize
the similarity of two environment rollouts.We train the model to correctly rank
rollouts according to this measure in addition to predicting the correct
reward. Empirically, we find that this approach leads to substantial
performance improvements on the PHYRE benchmark for physical reasoning: our
approach obtains a new state-of-the-art on that benchmark.
