Gabel, T., Riedmiller, M.A.: Cbr for state value function approximation in rein- forcement learning. In Mun˜oz-Avila, H., Ricci, F., eds.: ICCBR. Volume 3620 of Lecture Notes in Computer Science., Springer (
Basic Idea of State Function Approximation Instead of looking up on a State-Action table, we build a black box with weights inside it. Just tell the blackbox whose value functions we want, and then it will calculate and output the value. The weights can be learned by data, which is a ...
function [A,B,C,D,Mean0,Cov0,StateType] = timeInvariantParamMap(params) % Time-invariant state-space model parameter mapping function example. This % function maps the vector params to the state-space matrices (A, B, C, and % D), the initial state value and the initial state variance...
We parametrizemφusing radial-basis-function models of the formmφ(t) = (k(1)(t, τ) ⊗ I)φ(1), in which\({\varphi }^{(1)}\in {{\mathbb{R}}}^{pD}\)is the vector of weights associated with the approximation for the mean,\({k}^{(1)}(t,\tau )\,=\)\...
A CMAC system is adapted with 8 tilings for the approximation of state value. The state space of the "success estimation" module consists of only the angle between the goal and the opponent. The module learns the state value while the player taking the behavior of the "single Dribble&Shoot...
Tile Coding: Tile coding is another well-known function approximator. Unlike the continuous methods such as radial basis functions (RBF), tile coding is a discretization method which is used in RL. It is a piece-wise constant approximation method that approximates the action-value functions by...
Deconvoluting cell-state abundances from bulk RNA-sequencing data can add considerable value to existing data, but achieving fine-resolution and high-accuracy deconvolution remains a challenge. Here we introduce MeDuSA, a mixed model-based method that leverages single-cell RNA-sequencing data as a ...
Specify optional pairs of arguments asName1=Value1,...,NameN=ValueN, whereNameis the argument name andValueis the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.
The reward function is not unique. If we set the reward function is a constant for any state and/or action, then any policy is optimal. Linear Feature Reward Inverse RL Recall linear value function approximation Similarly, here consider when reward is linear over features ...
"energy"— Bar chart of normalized energies If you do not specify this argument, the function plots the Hankel singular values and associated error bounds of the normalized coprime factorization of the original modelsys. Parent graphics container, specified as one of these objects: ...