Here I apply three reinforcement learning methods to the full, continuous action, swing-up
acrobot control benchmark problem. These include two approaches from the literature …

In this paper I investigate methods of applying reinforcement learning to continuous state-
and action-space problems without a policy function. I compare the performance of four …

In this study, invasive weed optimization (IWO) was used to investigate the optimum Q
values of the linear quadratic regulator (LQR) for the inverted balance control of the Robo …

To provide a high level dynamic stability objective for humanoid robots that takes into
consideration forces due to joint coupling, we derive an analytical solution to the dynamic …

Complex under-actuated multilink mechanism involves a system whose number of control
inputs is smaller than the dimension of the configuration space. The ability to control such a …

This work describes how genetic programming is applied to evolving controllers for the
minimum time swing up and inverted balance tasks of the continuous state and action …

Since decision-making algorithms on high-performance computing yield large-size policies,
compression methods are necessary for utilizing them on small robots or on robots that must …

Reinforcement learning in the continuous state-space poses the problem of the inability to
store the values of all state-action pairs in a lookup table, due to both storage limitations and …