RLupus:Cooperation through emergent communication in the Werewolf social deduction game

This paper focuses on the emergence of communication to support cooperation in environments modeled as social deduction games (SDG), that are games where players communicate freely to deduce each others' hidden intentions. We first state the problem by giving a general formalization of SDG and a possible solution framework based on reinforcement learning. Next, we focus on a specific SDG, known as The Werewolf, and study if and how various forms of communication influence the outcome of the game. Experimental results show that introducing a communication signal greatly increases the winning chances of a class of players. We also study the effect of the signal's length and range on the overall performance showing a non-linear relationship

Balanced Reward-inspired Reinforcement Learning for Autonomous Vehicle Racing

Autonomous vehicle racing has attracted extensive interest due to its great potential in autonomous driving at the extreme limits. Model-based and learning-based methods are being widely used in autonomous racing. However, model-based methods cannot cope with the dynamic environments when only local perception is available. As a comparison, learning-based methods can handle complex environments under local perception. Recently, deep reinforcement learning (DRL) has gained popularity in autonomous racing. DRL outperforms conventional learning- based methods by handling complex situations and leveraging local information. DRL algorithms, such as the proximal policy algorithm, can achieve a good balance between the execution time and safety in autonomous vehicle competition. However, the training outcomes of conventional DRL methods exhibit inconsistent correctness in decision-making. The instability in decision-making introduces safety concerns in autonomous vehicle racing, such as collisions into track boundaries. The proposed algorithm is capable to avoid collisions and improve the training quality. Simulation results on a physical engine demonstrate that the proposed algorithm outperforms other DRL algorithms in achieving safer control during sharp bends, fewer collisions into track boundaries, and higher training quality among multiple tracks

Decision-making of an autonomous vehicle in the presence of emergency vehicle using deep reinforcement learning

Autonomous Vehicles are the future of road transportation where they can increase safety, efficiency, and productivity. In this thesis, we address a new edge case in autonomous driving when one autonomous vehicle is approached by an emergency vehicle and needs to make the best decision. To achieve the desired behavior and learn the sequence decision process, we trained our autonomous vehicle with the help of Deep Reinforcement Learning algorithms and compared the results with rule-based algorithms. The driving environment for this study was developed by using Simulation Urban Mobility as an open-source traffic simulator. The proposed solution based on Deep Reinforcement Learning has a better performance compared to the rule-based solution as a baseline both in normal driving situations and when an emergency vehicle is approaching