  Reinforcement learning has demonstrated impressive performance in various
challenging problems such as robotics, board games, and classical arcade games.
However, its real-world applications can be hindered by the absence of
robustness and safety in the learned policies. More specifically, an RL agent
that trains in a certain Markov decision process (MDP) often struggles to
perform well in nearly identical MDPs. To address this issue, we employ the
framework of Robust MDPs (RMDPs) in a model-based setting and introduce a novel
learned transition model. Our method specifically incorporates an auxiliary
pessimistic model, updated adversarially, to estimate the worst-case MDP within
a Kullback-Leibler uncertainty set. In comparison to several existing works,
our work does not impose any additional conditions on the training environment,
such as the need for a parametric simulator. To test the effectiveness of the
proposed pessimistic model in enhancing policy robustness, we integrate it into
a practical RL algorithm, called Robust Model-Based Policy Optimization
(RMBPO). Our experimental results indicate a notable improvement in policy
robustness on high-dimensional MuJoCo control tasks, with the auxiliary model
enhancing the performance of the learned policy in distorted MDPs. We further
explore the learned deviation between the proposed auxiliary world model and
the nominal model, to examine how pessimism is achieved. By learning a
pessimistic world model and demonstrating its role in improving policy
robustness, our research contributes towards making (model-based) RL more
robust.
