  Deep reinforcement learning (RL) algorithms have recently achieved remarkable
successes in various sequential decision making tasks, leveraging advances in
methods for training large deep networks. However, these methods usually
require large amounts of training data, which is often a big problem for
real-world applications. One natural question to ask is whether learning good
representations for states and using larger networks helps in learning better
policies. In this paper, we try to study if increasing input dimensionality
helps improve performance and sample efficiency of model-free deep RL
algorithms. To do so, we propose an online feature extractor network (OFENet)
that uses neural nets to produce good representations to be used as inputs to
deep RL algorithms. Even though the high dimensionality of input is usually
supposed to make learning of RL agents more difficult, we show that the RL
agents in fact learn more efficiently with the high-dimensional representation
than with the lower-dimensional state observations. We believe that stronger
feature propagation together with larger networks (and thus larger search
space) allows RL agents to learn more complex functions of states and thus
improves the sample efficiency. Through numerical experiments, we show that the
proposed method outperforms several other state-of-the-art algorithms in terms
of both sample efficiency and performance. Codes for the proposed method are
available at http://www.merl.com/research/license/OFENet .
