Reinforcement learning of optimal active particle navigation

Automatic Robot Navigation Using Reinforcement Learning

Autonomous vehicle navigation using evolutionary reinforcement learning

Reinforcement learning schemes perform direct on-line search in control space. This makes them appropriate for modifying control rules to obtain improvements in the performance of a system. The effectiveness of a reinforcement learning strategy is studied here through the training of a learning classz$er system (LCS) that controls the movement of an autonomous vehicle in simulated paths includi...

Robotic Controllers for Navigation using Reinforcement-Learning

Understanding the human brain and its behaviour is the main aim of Neuroscience, therefore forming a model with the objective of imitating a special biological behaviour, like the ability to learn, is a research problem with many potential applications. This thesis aims to present a simulation of the Morris water maze [22] using a robot in order to compare two different Reinforcement Learning t...

A Reinforcement Learning Approach for Multiagent Navigation

This paper presents a Q-Learning-based multiagent system oriented to provide navigation skills to simulation agents in virtual environments. We focus on learning local navigation behaviours from the interactions with other agents and the environment. We adopt an environment-independent state space representation to provide the required scalability of such kind of systems. In this way, we evalua...

Hierarchical Reinforcement Learning for Robot Navigation

For complex tasks, such as manipulation and robot navigation, reinforcement learning (RL) is well-known to be difficult due to the curse of dimensionality. To overcome this complexity and making RL feasible, hierarchical RL (HRL) has been suggested. The basic idea of HRL is to divide the original task into elementary subtasks, which can be learned using RL. In this paper, we propose a HRL archi...