Generative Temporal Models with Spatial Memory for Partially Observed Environments

Understand

In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning mechanism.

• However, their application in practice has been limited to simplistic environments, due to the difficulty of training such models in larger, potentially partially-observed and 3D environments.
• In this work we introduce a novel action-conditioned generative model of such challenging environments.
• The model features a non-parametric spatial memory system in which we store learned, disentangled representations of the environment.