How can policy gradient fulfill the required action range?

As we often use the gaussian policy for continuous action space, but how can we make it proper for the range. In spiningup’s implementation, I found they just simply used, def mlp_gaussian_policy(x, a, hidden_sizes, activation, output_activation, action_space): act_dim = a.shape.as_list()[-1] mu = mlp(x, list(hidden_sizes)+[act_dim], activation, output_activation) log_std = tf.get_variable(name=’log_std’, initializer=-0.5*np.ones(act_dim, dtype=np.float32)) std = tf.exp(log_std) pi…

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How to format json data to array format and nested arrays are in object like {} without square brackets in typscript [closed]

Now return data from rest api as : { “ProductID”:1, “Category”:[ { “CategoryID”:1, “SubCategory”:[ { “SubCategoryID”:1, } ] } ] } I want to converted output data manipulated in typescript as: [ { “ProductID”:1, “Category”:{ “CategoryID”:1, “SubCategory”:{ “SubCategoryID”:1, `enter code here` } } } ] I have tried return this.restApi.getProductBin().subscribe((data: {}) => { const usersJson:…

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