Intelligent Flapping Wing Control: Reinforcement Learning for the DelFly

Flight control of the DelFly is challenging, because of its complex dynamics and variability due to manufacturing inconsistencies. Machine Learning algorithms can be used to tackle these challenges. A Policy Gradient algorithm is used to tune the gains of a Proportional-Integral controller using Reinforcement Learning. Furthermore, a novel...

Use of Affordances for Efficient Robot Learning

Automatic synthesis of supervisory control systems

Sequential composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers sequentially to achieve a control specification that cannot be realized by a single controller. Sequential composition focuses on the interaction between a collection of pre-designed...

Deep Reinforcement Learning - Pretraining actor-critic networks using state representation learning

In control, the objective is to find a mapping from states to actions that steer a system to a desired reference. A controller can be designed by an engineer, typically using some model of the system or it can be learned by an algorithm. Reinforcement Learning (RL) is one such algorithm. In RL, the controller is an agent that interacts with the...

Control by Interconnection using Reinforcement Learning

The dynamics of many physical processes can be described by port-Hamiltonian (PH) models where the importance of the energy function can be seen. In Control by Interconnection (CbI), the controller is another PH system that is connected to the plant through a power preserving interconnection to add up the energy functions. However, a major issue...

Reward system design for incorporating control performance

Reinforcement learning (RL) is a machine learning technique whereby the controller learns the control law by optimizing the received cumulative amount of reward. A reward is an instantaneous evaluation of the applied action at the current state, given by reward function. However in theory the reward function is assumed to be given, in practice...

Multi-Agent Actor-Critic Reinforcement Learning for Cooperative Tasks

For single-agent problems, Reinforcement Learning (RL) algorithms proved to be useful learning optimal control laws for nonlinear dynamic systems without relying on a mathematical model of the system to be controlled. With their ability to work on continuous action and state spaces, actor-critic RL algorithms are especially advantageous in that...