  Incomplete knowledge of the environment leads an agent to make decisions
under uncertainty. One of the major dilemmas in Reinforcement Learning (RL)
where an autonomous agent has to balance two contrasting needs in making its
decisions is: exploiting the current knowledge of the environment to maximize
the cumulative reward as well as exploring actions that allow improving the
knowledge of the environment, hopefully leading to higher reward values
(exploration-exploitation trade-off). Concurrently, another relevant issue
regards the full observability of the states, which may not be assumed in all
applications. For instance, when 2D images are considered as input in an RL
approach used for finding the best actions within a 3D simulation environment.
In this work, we address these issues by deploying and testing several
techniques to balance exploration and exploitation trade-off on partially
observable systems for predicting steering wheels in autonomous driving
scenarios. More precisely, the final aim is to investigate the effects of using
both adaptive and deterministic exploration strategies coupled with a Deep
Recurrent Q-Network. Additionally, we adapted and evaluated the impact of a
modified quadratic loss function to improve the learning phase of the
underlying Convolutional Recurrent Neural Network. We show that adaptive
methods better approximate the trade-off between exploration and exploitation
and, in general, Softmax and Max-Boltzmann strategies outperform epsilon-greedy
techniques.
