emote-rl 22.0.0

A modular reinforcement learning library

🍒 emote

Embark's Modular Training Engine - a flexible framework for reinforcement learning

🚧 This project is very much work in progress and not yet ready for production use. 🚧

What it does

Emote provides a way to build reusable components for creating reinforcement learning algorithms, and a library of premade componenents built in this way. It is strongly inspired by the callback setup used by Keras and FastAI.

As an example, let us see how the SAC, the Soft Actor Critic algorithm by Haarnoja et al. can be written using Emote. The main algorithm in SAC is given in Soft Actor-Critic Algorithms and Applications and looks like this:

Using the components provided with Emote, we can write this as

device = torch.device("cpu")
env = DictGymWrapper(AsyncVectorEnv(10 * [HitTheMiddle]))
table = DictObsTable(spaces=env.dict_space, maxlen=1000, device=device)
memory_proxy = TableMemoryProxy(table)
dataloader = MemoryLoader(table, 100, 2, "batch_size")

q1 = QNet(2, 1)
q2 = QNet(2, 1)
policy = Policy(2, 1)
ln_alpha = torch.tensor(1.0, requires_grad=True)
agent_proxy = FeatureAgentProxy(policy, device)

callbacks = [
    QLoss(name="q1", q=q1, opt=Adam(q1.parameters(), lr=8e-3)),
    QLoss(name="q2", q=q2, opt=Adam(q2.parameters(), lr=8e-3)),
    PolicyLoss(pi=policy, ln_alpha=ln_alpha, q=q1, opt=Adam(policy.parameters())),
    AlphaLoss(pi=policy, ln_alpha=ln_alpha, opt=Adam([ln_alpha]), n_actions=1),
    QTarget(pi=policy, ln_alpha=ln_alpha, q1=q1, q2=q2),
    SimpleGymCollector(env, agent_proxy, memory_proxy, warmup_steps=500),
    FinalLossTestCheck([logged_cbs[2]], [10.0], 2000),
]

trainer = Trainer(callbacks, dataloader)
trainer.train()

Here each callback in the callbacks list is its own reusable class that can readily be used for other similar algorithms. The callback classes themselves are very straight forward to write. As an example, here is the PolicyLoss callback.

class PolicyLoss(LossCallback):
    def __init__(
        self,
        *,
        pi: nn.Module,
        ln_alpha: torch.tensor,
        q: nn.Module,
        opt: optim.Optimizer,
        max_grad_norm: float = 10.0,
        name: str = "policy",
        data_group: str = "default",
    ):
        super().__init__(
            name=name,
            optimizer=opt,
            network=pi,
            max_grad_norm=max_grad_norm,
            data_group=data_group,
        )
        self.policy = pi
        self._ln_alpha = ln_alpha
        self.q1 = q
        self.q2 = q2

    def loss(self, observation):
        p_sample, logp_pi = self.policy(**observation)
        q_pi_min = self.q1(p_sample, **observation)
        # using reparameterization trick
        alpha = torch.exp(self._ln_alpha).detach()
        policy_loss = alpha * logp_pi - q_pi_min
        policy_loss = torch.mean(policy_loss)
        assert policy_loss.dim() == 0
        return policy_loss

Installation

For installation and environment handling we use pdm. Install it from pdm. After pdm is set up, set up and activate the emote environment by running

pdm install

Contribution

We welcome community contributions to this project.

Please read our Contributor Guide for more information on how to get started. Please also read our Contributor Terms before you make any contributions.

Any contribution intentionally submitted for inclusion in an Embark Studios project, shall comply with the Rust standard licensing model (MIT OR Apache 2.0) and therefore be dual licensed as described below, without any additional terms or conditions:

License

This contribution is dual licensed under EITHER OF

• Apache License, Version 2.0, (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

For clarity, "your" refers to Embark or any other licensee/user of the contribution.