Function-Approximation

Notes on DQN and its variants. ...

So far in the series, we have been choosing the actions based on the estimated action value function. On the other hand, we can instead learn a parameterized policy, $\boldsymbol{\theta}$, that can select actions without consulting a value function by updating $\boldsymbol{\theta}$ on each step in the direction of an estimate of the gradient of some performance measure w.r.t $\boldsymbol{\theta}$. Such methods are called policy gradient methods. ...

Beside $n$-step TD methods, there is another mechanism called eligible traces that unify TD and Monte Carlo. Setting $\lambda$ in TD($\lambda$) from $0$ to $1$, we end up with a spectrum ranging from TD methods, when $\lambda=0$ to Monte Carlo methods with $\lambda=1$. ...

All of the tabular methods we have been considering so far might scale well within a small state space. However, when dealing with Reinforcement Learning problems in continuous state space, an exact solution is nearly impossible to find. But instead, an approximated answer could be found. ...