b900fdf6f2
Closes #947 This removes all kwargs from all policy constructors. While doing that, I also improved several names and added a whole lot of TODOs. ## Functional changes: 1. Added possibility to pass None as `critic2` and `critic2_optim`. In fact, the default behavior then should cover the absolute majority of cases 2. Added a function called `clone_optimizer` as a temporary measure to support passing `critic2_optim=None` ## Breaking changes: 1. `action_space` is no longer optional. In fact, it already was non-optional, as there was a ValueError in BasePolicy.init. So now several examples were fixed to reflect that 2. `reward_normalization` removed from DDPG and children. It was never allowed to pass it as `True` there, an error would have been raised in `compute_n_step_reward`. Now I removed it from the interface 3. renamed `critic1` and similar to `critic`, in order to have uniform interfaces. Note that the `critic` in DDPG was optional for the sole reason that child classes used `critic1`. I removed this optionality (DDPG can't do anything with `critic=None`) 4. Several renamings of fields (mostly private to public, so backwards compatible) ## Additional changes: 1. Removed type and default declaration from docstring. This kind of duplication is really not necessary 2. Policy constructors are now only called using named arguments, not a fragile mixture of positional and named as before 5. Minor beautifications in typing and code 6. Generally shortened docstrings and made them uniform across all policies (hopefully) ## Comment: With these changes, several problems in tianshou's inheritance hierarchy become more apparent. I tried highlighting them for future work. --------- Co-authored-by: Dominik Jain <d.jain@appliedai.de>
65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
from collections.abc import Iterator
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from typing import TypeVar
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import torch
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from torch import nn
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def optim_step(
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loss: torch.Tensor,
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optim: torch.optim.Optimizer,
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module: nn.Module,
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max_grad_norm: float | None = None,
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) -> None:
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"""Perform a single optimization step.
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:param loss:
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:param optim:
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:param module:
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:param max_grad_norm: if passed, will clip gradients using this
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"""
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optim.zero_grad()
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loss.backward()
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if max_grad_norm:
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nn.utils.clip_grad_norm_(module.parameters(), max_norm=max_grad_norm)
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optim.step()
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_STANDARD_TORCH_OPTIMIZERS = [
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torch.optim.Adam,
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torch.optim.SGD,
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torch.optim.RMSprop,
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torch.optim.Adadelta,
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torch.optim.AdamW,
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torch.optim.Adamax,
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torch.optim.NAdam,
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torch.optim.SparseAdam,
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torch.optim.LBFGS,
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]
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TOptim = TypeVar("TOptim", bound=torch.optim.Optimizer)
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def clone_optimizer(
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optim: TOptim,
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new_params: nn.Parameter | Iterator[nn.Parameter],
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) -> TOptim:
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"""Clone an optimizer to get a new optim instance with new parameters.
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**WARNING**: This is a temporary measure, and should not be used in downstream code!
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Once tianshou interfaces have moved to optimizer factories instead of optimizers,
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this will be removed.
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:param optim: the optimizer to clone
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:param new_params: the new parameters to use
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:return: a new optimizer with the same configuration as the old one
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"""
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optim_class = type(optim)
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# custom optimizers may not behave as expected
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if optim_class not in _STANDARD_TORCH_OPTIMIZERS:
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raise ValueError(
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f"Cannot clone optimizer {optim} of type {optim_class}"
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f"Currently, only standard torch optimizers are supported.",
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)
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return optim_class(new_params, **optim.defaults)
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