  Offline reinforcement learning (RL) refers to the problem of learning
policies entirely from a large batch of previously collected data. This problem
setting offers the promise of utilizing such datasets to acquire policies
without any costly or dangerous active exploration. However, it is also
challenging, due to the distributional shift between the offline training data
and those states visited by the learned policy. Despite significant recent
progress, the most successful prior methods are model-free and constrain the
policy to the support of data, precluding generalization to unseen states. In
this paper, we first observe that an existing model-based RL algorithm already
produces significant gains in the offline setting compared to model-free
approaches. However, standard model-based RL methods, designed for the online
setting, do not provide an explicit mechanism to avoid the offline setting's
distributional shift issue. Instead, we propose to modify the existing
model-based RL methods by applying them with rewards artificially penalized by
the uncertainty of the dynamics. We theoretically show that the algorithm
maximizes a lower bound of the policy's return under the true MDP. We also
characterize the trade-off between the gain and risk of leaving the support of
the batch data. Our algorithm, Model-based Offline Policy Optimization (MOPO),
outperforms standard model-based RL algorithms and prior state-of-the-art
model-free offline RL algorithms on existing offline RL benchmarks and two
challenging continuous control tasks that require generalizing from data
collected for a different task. The code is available at
https://github.com/tianheyu927/mopo.
