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import torch
class ParameterisedPolicy(torch.nn.Module):
"""
REINFORCE RL agent class. Returns action when the ParameterisedPolicy.act(observation) method is used.
observation is a gym state vector.
obs_len - length of the state vector.
act_space_len - length of the action vector.
"""
def __init__(self, obs_len=8, act_space_len=2):
super().__init__()
self.obs_len = obs_len
self.act_space_len = act_space_len
self.lin_1 = torch.nn.Linear(self.obs_len, 256)
self.rel_1 = torch.nn.ReLU()
self.lin_2 = torch.nn.Linear(256, 128)
self.rel_2 = torch.nn.ReLU()
self.lin_3 = torch.nn.Linear(128, self.act_space_len)
self.lin_4 = torch.nn.Linear(128, self.act_space_len)
self.elu = torch.nn.ELU()
def forward(self, x):
x = self.lin_1(x)
x = self.rel_1(x)
x = self.lin_2(x)
x = self.rel_2(x)
mu = self.lin_3(x)
x = self.lin_4(x)
sigma = self.elu(x) + 1.000001
return mu, sigma
def act(self, observation):
"""
Method returns action when gym state vector is passed.
"""
(mus, sigmas) = self.forward(torch.tensor(observation, dtype=torch.float32))
m = torch.distributions.normal.Normal(mus, sigmas)
action = m.sample().detach().numpy()
return action |