  Model-free reinforcement learning methods such as the Proximal Policy
Optimization algorithm (PPO) have successfully applied in complex
decision-making problems such as Atari games. However, these methods suffer
from high variances and high sample complexity. On the other hand, model-based
reinforcement learning methods that learn the transition dynamics are more
sample efficient, but they often suffer from the bias of the transition
estimation. How to make use of both model-based and model-free learning is a
central problem in reinforcement learning. In this paper, we present a new
technique to address the trade-off between exploration and exploitation, which
regards the difference between model-free and model-based estimations as a
measure of exploration value. We apply this new technique to the PPO algorithm
and arrive at a new policy optimization method, named Policy Optimization with
Model-based Explorations (POME). POME uses two components to predict the
actions' target values: a model-free one estimated by Monte-Carlo sampling and
a model-based one which learns a transition model and predicts the value of the
next state. POME adds the error of these two target estimations as the
additional exploration value for each state-action pair, i.e, encourages the
algorithm to explore the states with larger target errors which are hard to
estimate. We compare POME with PPO on Atari 2600 games, and it shows that POME
outperforms PPO on 33 games out of 49 games.
