During training, agents are initialized in the starting random positions in the bottom of their environments (x ∈ [0.4, 0.6] and v = 0 for the Mountain Car and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\theta }_{1},{\theta }_{2},{\dot{\theta }}_{1},{\dot{\theta }}_{2}\,\in \,[\,-\,0.1,01]$$\end{document}θ1,θ2,θ˙1,θ˙2∈[−0.1,01] for the Acrobot). The state of the neural network is randomized and the initial parameters hi and Jij are set to zero. Agents are simulated for 1000 trials of 5000 steps, computing each trial the values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${m}_{i}^{m}$$\end{document}mim and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${c}_{ij}^{m}$$\end{document}cijm and applying Equation 3 at the end of the trial. The agent’s position and state are reset every 5 · 104 simulation steps.