PyTorch 1.x Reinforcement Learning Cookbook

How to do it...

We develop the REINFORCE algorithm to solve the CartPole environment as follows:

1. Import all the necessary packages and create a CartPole instance:

>>> import gym>>> import torch>>> import torch.nn as nn>>> env = gym.make('CartPole-v0')

2. Let's start with the __init__method of the PolicyNetwork class, which approximates the policy using a neural network:

>>> class PolicyNetwork(): ...     def __init__(self, n_state, n_action, n_hidden=50, lr=0.001): ...         self.model = nn.Sequential( ...                         nn.Linear(n_state, n_hidden), ...                         nn.ReLU(), ...                         nn.Linear(n_hidden, n_action), ...                         nn.Softmax(), ...                 ) ...         self.optimizer = torch.optim.Adam(                         self.model.parameters(), lr)

3. Next, add the predict method, which computes the estimated policy:

>>> ...