  One of the bottlenecks of training autonomous vehicle (AV) agents is the
variability of training environments. Since learning optimal policies for
unseen environments is often very costly and requires substantial data
collection, it becomes computationally intractable to train the agent on every
possible environment or task the AV may encounter. This paper introduces a
zero-shot filtering approach to interpolate learned policies of past
experiences to generalize to unseen ones. We use an experience kernel to
correlate environments. These correlations are then exploited to produce
policies for new tasks or environments from learned policies. We demonstrate
our methods on an autonomous vehicle driving through T-intersections with
different characteristics, where its behavior is modeled as a partially
observable Markov decision process (POMDP). We first construct compact
representations of learned policies for POMDPs with unknown transition
functions given a dataset of sequential actions and observations. Then, we
filter parameterized policies of previously visited environments to generate
policies to new, unseen environments. We demonstrate our approaches on both an
actual AV and a high-fidelity simulator. Results indicate that our experience
filter offers a fast, low-effort, and near-optimal solution to create policies
for tasks or environments never seen before. Furthermore, the generated new
policies outperform the policy learned using the entire data collected from
past environments, suggesting that the correlation among different environments
can be exploited and irrelevant ones can be filtered out.
