torchreinforce

A pythonic implementation of the REINFORCE algorithm that is actually fun to use

Installation

You can install it with pip as you would for any other python package

pip install torchreinforce

Quickstart

In order to use the REINFORCE algorithm with your model you only need to do two things:

• Use the ReinforceModule class as your base class
• Decorate your forward function with @ReinforceModule.forward

That's it!

class Model(ReinforceModule):
    def __init__(self, **kwargs):
        super(Model, self).__init__(**kwargs)
        self.net = torch.nn.Sequential(
            torch.nn.Linear(20, 128),
            torch.nn.ReLU(),
            torch.nn.Linear(128, 2),
            torch.nn.Softmax(dim=-1),
        )

    @ReinforceModule.forward
    def forward(self, x):
        return self.net(x)

Your model will now output ReinforceOutput objects.

This objects have two important functions

• get()
• reward(value)

You can use output.get() to get an actual sample of the overlaying distribution and output.reward(value) to set a reward for the specific output.

Being net your model you have to do something like that

action = net(observation)
observation, reward, done, info = env.step(action.get())
action.reward(reward)

Wait, did you just said distribution?

Yes! As the REINFORCE algorithm states the outputs of your model will be used as parameters for a probability distribution function.

Actually you can use whatever probability distribution you want, the ReinforceModule constructor accepts indeed the following parameters:

• gamma the gamma parameter of the REINFORCE algorithm (default: Categorical)
• distribution every ReinforceDistribution or pytorch.distributions distribution (default: 0.99)

like that

net = Model(distribution=torch.distributions.Beta, gamma=0.99)

Keep in mind that the outputs of your decorated forward(x) outputs will be used as the parameters for the distribution. If your distribution needs more than one parameters just return a list.

I've added the possibility to distribution to have a deterministic behavior in testing and I've implemented it only for the Categorical distribution, if you want to implement your own deterministic logic check the file distributions/categorical.py it is pretty straightforward

If you want to use the torch.distributions.Beta distribution for example you will need to do something like

class Model(ReinforceModule):
    def __init__(self, **kwargs):
        super(Model, self).__init__(**kwargs)
        ...

    @ReinforceModule.forward
    def forward(self, x):
        return [self.net1(x), self.net2(x)] # the Beta distribution accepts two parameters

net = Model(distribution=torch.distributions.Beta, gamma=0.99)

action = net(inp)
env.step(action.get())

Nice! What about training?

You can compute the REINFORCE loss by calling the loss() function of ReinforceModule and than treat it as you would do with any other pytorch loss function

net = ...
optmizer = ...

while training:
    net.reset()
    for steps:
        ....

    loss = net.loss(normalize=True)

    optimizer.zero_grad()
    loss.backward()
    optmizer.step()

You have to call the reset() function of ReinforceModule before the beginning of each episode. You can also pass the argument normalize to loss() if you want to normalize the rewards

Putting all together

A complete example looks like this:

class Model(ReinforceModule):
    def __init__(self, **kwargs):
        super(Model, self).__init__(**kwargs)
        self.net = torch.nn.Sequential(
            torch.nn.Linear(4, 128),
            torch.nn.ReLU(),
            torch.nn.Linear(128, 2),
            torch.nn.Softmax(dim=-1),
        )

    @ReinforceModule.forward
    def forward(self, x):
        return self.net(x)


env = gym.make('CartPole-v0')
net = Model()
optimizer = torch.optim.Adam(net.parameters(), lr=0.001)

for i in range(EPISODES):
    done = False
    net.reset()
    observation = env.reset()
    while not done:
        action = net(torch.tensor(observation, dtype=torch.float32))

        observation, reward, done, info = env.step(action.get())
        action.reward(reward)

    loss = net.loss(normalize=False)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

You can find a running example in the examples/ folder.