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# %% If one wishes to check the formulae behind the corrections of the moments,
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# simply choose the desired power:
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power = 4
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jd.m_formula(power = power)
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# <FILESEP>
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import numbers
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import math
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from collections import namedtuple
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import numpy as np
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import torch
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import torch.nn.functional as F
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import time
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import sys
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from torch.autograd import Variable
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LogField = namedtuple('LogField', ('data', 'plot', 'x_axis', 'divide_by'))
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def merge_stat(src, dest):
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for k, v in src.items():
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if not k in dest:
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dest[k] = v
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elif isinstance(v, numbers.Number):
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dest[k] = dest.get(k, 0) + v
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elif isinstance(v, np.ndarray): # for rewards in case of multi-agent
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dest[k] = dest.get(k, 0) + v
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else:
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if isinstance(dest[k], list) and isinstance(v, list):
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dest[k].extend(v)
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elif isinstance(dest[k], list):
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dest[k].append(v)
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else:
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dest[k] = [dest[k], v]
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def normal_entropy(std):
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var = std.pow(2)
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entropy = 0.5 + 0.5 * torch.log(2 * var * math.pi)
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return entropy.sum(1, keepdim=True)
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def normal_log_density(x, mean, log_std, std):
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var = std.pow(2)
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log_density = -(x - mean).pow(2) / (2 * var) - 0.5 * math.log(2 * math.pi) - log_std
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return log_density.sum(1, keepdim=True)
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def multinomials_log_density(actions, log_probs):
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log_prob = 0
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for i in range(len(log_probs)):
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log_prob += log_probs[i].gather(1, actions[:, i].long().unsqueeze(1))
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return log_prob
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def multinomials_log_densities(actions, log_probs):
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log_prob = [0] * len(log_probs)
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for i in range(len(log_probs)):
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log_prob[i] += log_probs[i].gather(1, actions[:, i].long().unsqueeze(1))
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log_prob = torch.cat(log_prob, dim=-1)
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return log_prob
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def get_flat_params_from(model):
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params = []
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for param in model.parameters():
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params.append(param.data.view(-1))
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flat_params = torch.cat(params)
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return flat_params
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def set_flat_params_to(model, flat_params):
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prev_ind = 0
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for param in model.parameters():
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flat_size = int(np.prod(list(param.size())))
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param.data.copy_(
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flat_params[prev_ind:prev_ind + flat_size].view(param.size()))
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prev_ind += flat_size
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def get_flat_grad_from(net, grad_grad=False):
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grads = []
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for param in net.parameters():
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if grad_grad:
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grads.append(param.grad.grad.view(-1))
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else:
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grads.append(param.grad.view(-1))
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flat_grad = torch.cat(grads)
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return flat_grad
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class Timer:
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def __init__(self, msg, sync=False):
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self.msg = msg
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self.sync = sync
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def __enter__(self):
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self.start = time.time()
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return self
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