  Informed and robust decision making in the face of uncertainty is critical
for robots that perform physical tasks alongside people. We formulate this as
Bayesian Reinforcement Learning over latent Markov Decision Processes (MDPs).
While Bayes-optimality is theoretically the gold standard, existing algorithms
do not scale well to continuous state and action spaces. Our proposal builds on
the following insight: in the absence of uncertainty, each latent MDP is easier
to solve. We first obtain an ensemble of experts, one for each latent MDP, and
fuse their advice to compute a baseline policy. Next, we train a Bayesian
residual policy to improve upon the ensemble's recommendation and learn to
reduce uncertainty. Our algorithm, Bayesian Residual Policy Optimization
(BRPO), imports the scalability of policy gradient methods and task-specific
expert skills. BRPO significantly improves the ensemble of experts and
drastically outperforms existing adaptive RL methods.
