  We consider model-based reinforcement learning (MBRL) in 2-agent,
high-fidelity continuous control problems -- an important domain for robots
interacting with other agents in the same workspace. For non-trivial dynamical
systems, MBRL typically suffers from accumulating errors. Several recent
studies have addressed this problem by learning latent variable models for
trajectory segments and optimizing over behavior in the latent space. In this
work, we investigate whether this approach can be extended to 2-agent
competitive and cooperative settings. The fundamental challenge is how to learn
models that capture interactions between agents, yet are disentangled to allow
for optimization of each agent behavior separately. We propose such models
based on a disentangled variational auto-encoder, and demonstrate our approach
on a simulated 2-robot manipulation task, where one robot can either help or
distract the other. We show that our approach has better sample efficiency than
a strong model-free RL baseline, and can learn both cooperative and adversarial
behavior from the same data.
