  Robust real-world learning should benefit from both demonstrations and
interactions with the environment. Current approaches to learning from
demonstration and reward perform supervised learning on expert demonstration
data and use reinforcement learning to further improve performance based on the
reward received from the environment. These tasks have divergent losses which
are difficult to jointly optimize and such methods can be very sensitive to
noisy demonstrations. We propose a unified reinforcement learning algorithm,
Normalized Actor-Critic (NAC), that effectively normalizes the Q-function,
reducing the Q-values of actions unseen in the demonstration data. NAC learns
an initial policy network from demonstrations and refines the policy in the
environment, surpassing the demonstrator's performance. Crucially, both
learning from demonstration and interactive refinement use the same objective,
unlike prior approaches that combine distinct supervised and reinforcement
losses. This makes NAC robust to suboptimal demonstration data since the method
is not forced to mimic all of the examples in the dataset. We show that our
unified reinforcement learning algorithm can learn robustly and outperform
existing baselines when evaluated on several realistic driving games.
