Abstract
Reinforcement learning techniques bring a new perspective to enduring problems. Developing skills from scratch is not only appealing due to the artificial creation of knowledge. It can also replace years of work and refinement in a matter of hours. From all the developed skills in the RoboCup 3D Soccer Simulation League, running is still considerably relevant to determine the winner of any match. However, current approaches do not make full use of the robotic soccer agents’ potential. To narrow this gap, we propose a way of leveraging the Proximal Policy Optimization using the information provided by the simulator for official RoboCup matches. To do this, our algorithm uses a mix of raw, computed and internally generated data. The final result is a sprinting and a stopping behavior that work in tandem to bring the agent from point a to point b in a very short time. The sprinting speed stabilizes at around 2.5 m/s, which is a great improvement over current solutions. Both the sprinting and stopping behaviors are remarkably stable. © 2019, Springer Nature Switzerland AG.

Abstract
Economic and Food Safety Authority receives on a daily basis reports and complaints regarding infractions, delicts and possible food and economic crimes. These reports and complaints can be in different forms, such as e-mails, online forms, letters, phone calls and complaint books present in every establishment. This paper aims to apply text mining and classification algorithms to textual data extracted from these reports and complains in order to help identify if the responsible entity to analyze the content is, in fact, the Economic and Food Safety Authority. The paper describes text preprocessing and feature extraction procedures applied to Portuguese text data. Supervised multi-class classification methods such as Naïve Bayes and Support Vector Machine Classifiers are employed in the task. We show that a non-semantical text mining approach can achieve good results, scoring around 70% of accuracy.

Abstract
Deep learning models have as of late risen as popular function approximators for single-agent reinforcement learning challenges, by accurately estimating the value function of complex environments and being able to generalize to new unseen states. For multi-agent fields, agents must cope with the non-stationarity of the environment, due to the presence of other agents, and can take advantage of information sharing techniques for improved coordination. We propose an neural-based actor-critic algorithm, which learns communication protocols between agents and implicitly shares information during the learning phase. Large numbers of agents communicate with a self-learned protocol during distributed execution, and reliably learn complex strategies and protocols for partially observable multi-agent environments. © Springer Nature Switzerland AG 2019.

Abstract
Given the plethora of Reinforcement Learning algorithms available in the literature, it can prove challenging to decide on the most appropriate one to use in order to solve a given Reinforcement Learning task. This work presents a benchmark study on the performance of several Reinforcement Learning algorithms for discrete learning environments. The study includes several deep as well as non-deep learning algorithms, with special focus on the Deep Q-Network algorithm and its variants. Neural Fitted Q-Iteration, the predecessor of Deep Q-Network as well as Vanilla Policy Gradient and a planner were also included in this assessment in order to provide a wider range of comparison between different approaches and paradigms. Three learning environments were used in order to carry out the tests, including a 2D maze and two OpenAI Gym environments, namely a custom-built Foraging/Tagging environment and the CartPole environment.

Abstract
This paper investigates the learning of both low-level behaviors for humanoid robot controllers and of high-level coordination strategies for teams of robots engaged in simulated soccer. Regarding controllers, current approaches typically hand-tune behaviors or optimize them without realistic constraints, for example allowing parts of the robot to intersect with others. This level of optimization often leads to low-performance behaviors. Regarding strategies, most are hand-tuned with arbitrary parameters (like agents moving to pre-defined positions on the field such that eventually they can score a goal) and the thorough analysis of learned strategies is often disregarded. This paper demonstrates how it is possible to use a distributed framework to learn both low-level behaviors, like sprinting and getting up, and high-level strategies, like a kick-off scenario, outperforming previous approaches in the FCPortugal3D Simulated Soccer team.

Abstract
Over the last years, robotics has increased its interest in learning human-like behaviors and activities. One of the most common actions searched, as well as one of the most fun to replicate, is the ability to play sports. This has been made possible with the steady increase of automated learning, encouraged by the tremendous developments in computational power and improved reinforcement learning (RL) algorithms. This paper implements a beginner Robot player for precision ball sports like boccia and bocce. A new simulated environment (PrecisionBall) is created, and a seven degree-of-freedom (DoF) robotic arm, is able to learn from scratch how to win the game and throw different types of balls towards the goal (the jack), using deep reinforcement learning. The environment is compliant with OpenAI Gym, using the MuJoCo realistic physics engine for a realistic simulation. A brief comparison of the convergence of different RL algorithms is performed. Several ball weights and various types of materials correspondent to bocce and boccia are tested, as well as different friction coefficients. Results show that the robot achieves a maximum success rate of 92.7% and mean of 75.7% for the best case. While learning to play these sports with the DDPG+HER algorithm, the robotic agent acquired some relevant skills that allowed it to win.