Embark's Modular Training Engine - a flexible framework for reinforcement learning
🚧 This project is a work in progress. Things can and will change. 🚧
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 = MemoryTableProxy(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_lossFor package management and environment handling we use pants. Install it from pants. After pants is set up, verify that it is setup by running
pants tailor ::Box2d complains: Box2d needs swig and python bindings. On apt-based systems try
sudo apt install swig
sudo apt install python3.10-devPython 3.10 is tricky to install: For Ubuntu based distros try adding the deadsnakes PPA.
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- 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.
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