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rl/Lib/site-packages/stable_baselines3/common/sb2_compat/rmsprop_tf_like.py

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+from typing import Any, Callable, Dict, Iterable, Optional
+
+import torch
+from torch.optim import Optimizer
+
+
+class RMSpropTFLike(Optimizer):
+    r"""Implements RMSprop algorithm with closer match to Tensorflow version.
+
+    For reproducibility with original stable-baselines. Use this
+    version with e.g. A2C for stabler learning than with the PyTorch
+    RMSProp. Based on the PyTorch v1.5.0 implementation of RMSprop.
+
+    See a more throughout conversion in pytorch-image-models repository:
+        https://github.com/rwightman/pytorch-image-models/blob/master/timm/optim/rmsprop_tf.py
+
+    Changes to the original RMSprop:
+        - Move epsilon inside square root
+        - Initialize squared gradient to ones rather than zeros
+
+    Proposed by G. Hinton in his
+    `course <http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf>`_.
+
+    The centered version first appears in `Generating Sequences
+    With Recurrent Neural Networks <https://arxiv.org/pdf/1308.0850v5.pdf>`_.
+
+    The implementation here takes the square root of the gradient average before
+    adding epsilon (note that TensorFlow interchanges these two operations). The effective
+    learning rate is thus :math:`\alpha/(\sqrt{v} + \epsilon)` where :math:`\alpha`
+    is the scheduled learning rate and :math:`v` is the weighted moving average
+    of the squared gradient.
+
+    :params: iterable of parameters to optimize or dicts defining
+        parameter groups
+    :param lr: learning rate (default: 1e-2)
+    :param momentum: momentum factor (default: 0)
+    :param alpha: smoothing constant (default: 0.99)
+    :param eps: term added to the denominator to improve
+        numerical stability (default: 1e-8)
+    :param centered: if ``True``, compute the centered RMSProp,
+        the gradient is normalized by an estimation of its variance
+    :param weight_decay: weight decay (L2 penalty) (default: 0)
+
+    """
+
+    def __init__(
+        self,
+        params: Iterable[torch.nn.Parameter],
+        lr: float = 1e-2,
+        alpha: float = 0.99,
+        eps: float = 1e-8,
+        weight_decay: float = 0,
+        momentum: float = 0,
+        centered: bool = False,
+    ):
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= momentum:
+            raise ValueError(f"Invalid momentum value: {momentum}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+        if not 0.0 <= alpha:
+            raise ValueError(f"Invalid alpha value: {alpha}")
+
+        defaults = dict(lr=lr, momentum=momentum, alpha=alpha, eps=eps, centered=centered, weight_decay=weight_decay)
+        super().__init__(params, defaults)
+
+    def __setstate__(self, state: Dict[str, Any]) -> None:
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("momentum", 0)
+            group.setdefault("centered", False)
+
+    @torch.no_grad()
+    def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]:  # type: ignore[override]
+        """Performs a single optimization step.
+
+        :param closure: A closure that reevaluates the model
+            and returns the loss.
+        :return: loss
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            for p in group["params"]:
+                if p.grad is None:
+                    continue
+                grad = p.grad
+                if grad.is_sparse:
+                    raise RuntimeError("RMSpropTF does not support sparse gradients")
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state["step"] = 0
+                    # PyTorch initialized to zeros here
+                    state["square_avg"] = torch.ones_like(p, memory_format=torch.preserve_format)
+                    if group["momentum"] > 0:
+                        state["momentum_buffer"] = torch.zeros_like(p, memory_format=torch.preserve_format)
+                    if group["centered"]:
+                        state["grad_avg"] = torch.zeros_like(p, memory_format=torch.preserve_format)
+
+                square_avg = state["square_avg"]
+                alpha = group["alpha"]
+
+                state["step"] += 1
+
+                if group["weight_decay"] != 0:
+                    grad = grad.add(p, alpha=group["weight_decay"])
+
+                square_avg.mul_(alpha).addcmul_(grad, grad, value=1 - alpha)
+
+                if group["centered"]:
+                    grad_avg = state["grad_avg"]
+                    grad_avg.mul_(alpha).add_(grad, alpha=1 - alpha)
+                    # PyTorch added epsilon after square root
+                    # avg = square_avg.addcmul(grad_avg, grad_avg, value=-1).sqrt_().add_(group['eps'])
+                    avg = square_avg.addcmul(grad_avg, grad_avg, value=-1).add_(group["eps"]).sqrt_()
+                else:
+                    # PyTorch added epsilon after square root
+                    # avg = square_avg.sqrt().add_(group['eps'])
+                    avg = square_avg.add(group["eps"]).sqrt_()
+
+                if group["momentum"] > 0:
+                    buf = state["momentum_buffer"]
+                    buf.mul_(group["momentum"]).addcdiv_(grad, avg)
+                    p.add_(buf, alpha=-group["lr"])
+                else:
+                    p.addcdiv_(grad, avg, value=-group["lr"])
+
+        return loss