  Model-based reinforcement learning (RL) has shown great promise due to its
sample efficiency, but still struggles with long-horizon sparse-reward tasks,
especially in offline settings where the agent learns from a fixed dataset. We
hypothesize that model-based RL agents struggle in these environments due to a
lack of long-term planning capabilities, and that planning in a temporally
abstract model of the environment can alleviate this issue. In this paper, we
make two key contributions: 1) we introduce an offline model-based RL
algorithm, IQL-TD-MPC, that extends the state-of-the-art Temporal Difference
Learning for Model Predictive Control (TD-MPC) with Implicit Q-Learning (IQL);
2) we propose to use IQL-TD-MPC as a Manager in a hierarchical setting with any
off-the-shelf offline RL algorithm as a Worker. More specifically, we pre-train
a temporally abstract IQL-TD-MPC Manager to predict "intent embeddings", which
roughly correspond to subgoals, via planning. We empirically show that
augmenting state representations with intent embeddings generated by an
IQL-TD-MPC manager significantly improves off-the-shelf offline RL agents'
performance on some of the most challenging D4RL benchmark tasks. For instance,
the offline RL algorithms AWAC, TD3-BC, DT, and CQL all get zero or near-zero
normalized evaluation scores on the medium and large antmaze tasks, while our
modification gives an average score over 40.
