import torch

agents = {}

policyPars = {
    'neurons': (10, 200, 200, 200, 8),  # 5 box-related state, 4 goal-related state, 8 controls
    'act': ['F.leaky_relu', 'F.leaky_relu', 'F.leaky_relu'],
    'mu': torch.zeros(10),
    'std': torch.ones(10),
    'trainMode': False,  # Make sure both value and policy are set to the same thing
    'load': False,
    'dual': False,
    'beta': 20  # boltzmann. Increase for more certainty when making decisions
}
policyTrain = {
    'batch': 128,  # used to be 256
    'lr': 3e-4,
    'buffer': 10000,
    'gamma': .975,
    'explore': 0,  # Don't change this. Tune the exploration up top
    'double': True,
    'noise': 0,
    'priority': False,
    'update_target_network_every': 300,
    'train_every_steps': 1,  # note: ratio of env steps to gradient steps is always 1
    'explore_steps': 5000,
}
doubleQPars = {'valPars': policyPars, 'valTrain': policyTrain, 'agents': agents}