  The most data-efficient algorithms for reinforcement learning in robotics are
model-based policy search algorithms, which alternate between learning a
dynamical model of the robot and optimizing a policy to maximize the expected
return given the model and its uncertainties. However, the current algorithms
lack an effective exploration strategy to deal with sparse or misleading reward
scenarios: if they do not experience any state with a positive reward during
the initial random exploration, it is very unlikely to solve the problem. Here,
we propose a novel model-based policy search algorithm, Multi-DEX, that
leverages a learned dynamical model to efficiently explore the task space and
solve tasks with sparse rewards in a few episodes. To achieve this, we frame
the policy search problem as a multi-objective, model-based policy optimization
problem with three objectives: (1) generate maximally novel state trajectories,
(2) maximize the expected return and (3) keep the system in state-space regions
for which the model is as accurate as possible. We then optimize these
objectives using a Pareto-based multi-objective optimization algorithm. The
experiments show that Multi-DEX is able to solve sparse reward scenarios (with
a simulated robotic arm) in much lower interaction time than VIME, TRPO,
GEP-PG, CMA-ES and Black-DROPS.
