Graph convolution reinforcement learning for active wake control in windfarms: Application of a multi-agent reinforcement learning algorithm

Wind energy, generated by windfarms, is playing an increasingly critical role in meeting current and future energy demands. windfarms, however, face a challenge due to the inherent flaw of wake-induced power losses when turbines are located in close proximity. Wakes, characterized by regions of turbulence and lower wind speed, are created as air...

Evaluating Robustness of Deep Reinforcement Learning for Autonomous Driving: How does entropy maximization affect the training and robustness of final policies under various testing conditions?

This research paper aims to investigate the effect of entropy while training the agent on the robustness of the agent. This is important because robustness is defined as the agent's adaptability to different environments. A self-driving car should adapt to every environment that it is being used in since a mistake could cost someone's life....

Effects of action space discretization and DQN extensions on algorithm robustness and efficiency: How do the discretization of the action space and various extensions to the well-known DQN algorithm influence training and the robustness of final policies under various testing conditions?

Reinforcement Learning (RL) has gained atten-tion as a way of creating autonomous agents for self-driving cars. This paper explores the adap- tation of the Deep Q Network (DQN), a popular deep RL algorithm, in the Carla traffic simulator for autonomous driving. It investigates the influ- ence of action space discretization and DQN ex-<br/...

Learning the Problem Representation for Improving Negotiation Strategies

The domains of the negotiation can vary significantly. It is possible that a domain is very cooperative, where both agents can receive a high utility; the opposite is also possible, where the domain is very competitive and the agents cannot both get a high utility. In the same manner, the agents can have different strategies leading to a...

Using Decision Trees produced by Generative Adversarial Imitation Learning to give insight into black box Reinforcement Learning models

Machine learning models are increasingly being used in fields that have a direct impact on the lives of humans. Often these machine learning models are black-box models and they lack transparency and trust which is holding back the implementation. To increase transparency and trust this research investigates whether imitation learning,...

Knowing one’s opponents: Self Modeling Advantage Actor Critic for the Iterated Prisoner’s Dilemma

A recent advancement in Reinforcement Learning is the capability of modelling opponents. In this work, we are interested in going back to basics and testing this capability within the Iterated Prisoner's Dilemma, a simple method for modelling multi agent systems. Using the self modelling advantage actor critic model, we set up a single agent...

Reinforcement Learning for minimizing the total waiting time of passengers in a Taxi Dispatch Problem

A Taxi Dispatch Problem involves assigning taxis to requests of passengers who are waiting at different locations for a trip. In today's economy and society, the Taxi Dispatch Problem and other transport problems can be found everywhere. Not only in transporting people, but also in food delivery from restaurants and package delivery for all kind...