  Most deep reinforcement learning (RL) systems are not able to learn
effectively from off-policy data, especially if they cannot explore online in
the environment. These are critical shortcomings for applying RL to real-world
problems where collecting data is expensive, and models must be tested offline
before being deployed to interact with the environment -- e.g. systems that
learn from human interaction. Thus, we develop a novel class of off-policy
batch RL algorithms, which are able to effectively learn offline, without
exploring, from a fixed batch of human interaction data. We leverage models
pre-trained on data as a strong prior, and use KL-control to penalize
divergence from this prior during RL training. We also use dropout-based
uncertainty estimates to lower bound the target Q-values as a more efficient
alternative to Double Q-Learning. The algorithms are tested on the problem of
open-domain dialog generation -- a challenging reinforcement learning problem
with a 20,000-dimensional action space. Using our Way Off-Policy algorithm, we
can extract multiple different reward functions post-hoc from collected human
interaction data, and learn effectively from all of these. We test the
real-world generalization of these systems by deploying them live to converse
with humans in an open-domain setting, and demonstrate that our algorithm
achieves significant improvements over prior methods in off-policy batch RL.
