  Offline reinforcement learning requires reconciling two conflicting aims:
learning a policy that improves over the behavior policy that collected the
dataset, while at the same time minimizing the deviation from the behavior
policy so as to avoid errors due to distributional shift. This trade-off is
critical, because most current offline reinforcement learning methods need to
query the value of unseen actions during training to improve the policy, and
therefore need to either constrain these actions to be in-distribution, or else
regularize their values. We propose an offline RL method that never needs to
evaluate actions outside of the dataset, but still enables the learned policy
to improve substantially over the best behavior in the data through
generalization. The main insight in our work is that, instead of evaluating
unseen actions from the latest policy, we can approximate the policy
improvement step implicitly by treating the state value function as a random
variable, with randomness determined by the action (while still integrating
over the dynamics to avoid excessive optimism), and then taking a state
conditional upper expectile of this random variable to estimate the value of
the best actions in that state. This leverages the generalization capacity of
the function approximator to estimate the value of the best available action at
a given state without ever directly querying a Q-function with this unseen
action. Our algorithm alternates between fitting this upper expectile value
function and backing it up into a Q-function. Then, we extract the policy via
advantage-weighted behavioral cloning. We dub our method implicit Q-learning
(IQL). IQL demonstrates the state-of-the-art performance on D4RL, a standard
benchmark for offline reinforcement learning. We also demonstrate that IQL
achieves strong performance fine-tuning using online interaction after offline
initialization.
