  The learned policy of model-free offline reinforcement learning (RL) methods
is often constrained to stay within the support of datasets to avoid possible
dangerous out-of-distribution actions or states, making it challenging to
handle out-of-support region. Model-based RL methods offer a richer dataset and
benefit generalization by generating imaginary trajectories with either trained
forward or reverse dynamics model. However, the imagined transitions may be
inaccurate, thus downgrading the performance of the underlying offline RL
method. In this paper, we propose to augment the offline dataset by using
trained bidirectional dynamics models and rollout policies with double check.
We introduce conservatism by trusting samples that the forward model and
backward model agree on. Our method, confidence-aware bidirectional offline
model-based imagination, generates reliable samples and can be combined with
any model-free offline RL method. Experimental results on the D4RL benchmarks
demonstrate that our method significantly boosts the performance of existing
model-free offline RL algorithms and achieves competitive or better scores
against baseline methods.
