  Reduced models of large Markov decision processes accelerate planning by
considering a subset of outcomes for each state-action pair. This reduction in
reachable states leads to replanning when the agent encounters states without a
precomputed action during plan execution. However, not all states are suitable
for replanning. In the worst case, the agent may not be able to reach the goal
from the newly encountered state. Agents should be better prepared to handle
such risky situations and avoid replanning in risky states. Hence, we consider
replanning in states that are unsafe for deliberation as a negative side effect
of planning with reduced models. While the negative side effects can be
minimized by always using the full model, this defeats the purpose of using
reduced models. The challenge is to plan with reduced models, but somehow
account for the possibility of encountering risky situations. An agent should
thus only replan in states that the user has approved as safe for replanning.
To that end, we propose planning using a portfolio of reduced models, a
planning paradigm that minimizes the negative side effects of planning using
reduced models by alternating between different outcome selection approaches.
We empirically demonstrate the effectiveness of our approach on three domains:
an electric vehicle charging domain using real-world data from a university
campus and two benchmark planning problems.
