mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
428516056a
* Policy-classes cleanup and torch/tf unification. - Make Policy abstract. - Add `action_dist` to call to `extra_action_out_fn` (necessary for PPO torch). - Move some methods and vars to base Policy (from TFPolicy): num_state_tensors, ACTION_PROB, ACTION_LOGP and some more. * Fix `clip_action` import from Policy (should probably be moved into utils altogether). * - Move `is_recurrent()` and `num_state_tensors()` into TFPolicy (from DynamicTFPolicy). - Add config to all Policy c'tor calls (as 3rd arg after obs and action spaces). * Add `config` to c'tor call to TFPolicy. * Add missing `config` to c'tor call to TFPolicy in marvil_policy.py. * Fix test_rollout_worker.py::MockPolicy and BadPolicy classes (Policy base class is now abstract). * Fix LINT errors in Policy classes. * Implement StatefulPolicy abstract methods in test cases: test_multi_agent_env.py. * policy.py LINT errors. * Create a simple TestPolicy to sub-class from when testing Policies (reduces code in some test cases). * policy.py - Remove abstractmethod from `apply_gradients` and `compute_gradients` (these are not required iff `learn_on_batch` implemented). - Fix docstring of `num_state_tensors`. * Make QMIX torch Policy a child of TorchPolicy (instead of Policy). * QMixPolicy add empty implementations of abstract Policy methods. * Store Policy's config in self.config in base Policy c'tor. * - Make only compute_actions in base Policy's an abstractmethod and provide pass implementation to all other methods if not defined. - Fix state_batches=None (most Policies don't have internal states). * Cartpole tf learning. * Cartpole tf AND torch learning (in ~ same ts). * Cartpole tf AND torch learning (in ~ same ts). 2 * Cartpole tf (torch syntax-broken) learning (in ~ same ts). 3 * Cartpole tf AND torch learning (in ~ same ts). 4 * Cartpole tf AND torch learning (in ~ same ts). 5 * Cartpole tf AND torch learning (in ~ same ts). 6 * Cartpole tf AND torch learning (in ~ same ts). Pendulum tf learning. * WIP. * WIP. * SAC torch learning Pendulum. * WIP. * SAC torch and tf learning Pendulum and Cartpole after cleanup. * WIP. * LINT. * LINT. * SAC: Move policy.target_model to policy.device as well. * Fixes and cleanup. * Fix data-format of tf keras Conv2d layers (broken for some tf-versions which have data_format="channels_first" as default). * Fixes and LINT. * Fixes and LINT. * Fix and LINT. * WIP. * Test fixes and LINT. * Fixes and LINT. Co-authored-by: Sven Mika <sven@Svens-MacBook-Pro.local>
110 lines
3.4 KiB
Python
110 lines
3.4 KiB
Python
import numpy as np
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import logging
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from ray.rllib.utils.framework import try_import_torch
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torch, _ = try_import_torch()
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logger = logging.getLogger(__name__)
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try:
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import tree
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except (ImportError, ModuleNotFoundError) as e:
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logger.warning("`dm-tree` is not installed! Run `pip install dm-tree`.")
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raise e
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def huber_loss(x, delta=1.0):
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"""Reference: https://en.wikipedia.org/wiki/Huber_loss"""
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return torch.where(
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torch.abs(x) < delta,
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torch.pow(x, 2.0) * 0.5, delta * (torch.abs(x) - 0.5 * delta))
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def reduce_mean_ignore_inf(x, axis):
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"""Same as torch.mean() but ignores -inf values."""
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mask = torch.ne(x, float("-inf"))
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x_zeroed = torch.where(mask, x, torch.zeros_like(x))
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return torch.sum(x_zeroed, axis) / torch.sum(mask.float(), axis)
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def minimize_and_clip(optimizer, objective, var_list, clip_val=10):
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"""Minimized `objective` using `optimizer` w.r.t. variables in
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`var_list` while ensure the norm of the gradients for each
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variable is clipped to `clip_val`
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"""
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gradients = optimizer.compute_gradients(objective, var_list=var_list)
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for i, (grad, var) in enumerate(gradients):
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if grad is not None:
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gradients[i] = (torch.nn.utils.clip_grad_norm_(grad, clip_val),
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var)
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return gradients
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def sequence_mask(lengths, maxlen, dtype=None):
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"""
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Exact same behavior as tf.sequence_mask.
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Thanks to Dimitris Papatheodorou
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(https://discuss.pytorch.org/t/pytorch-equivalent-for-tf-sequence-mask/
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39036).
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"""
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if maxlen is None:
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maxlen = lengths.max()
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mask = ~(torch.ones((len(lengths), maxlen)).to(
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lengths.device).cumsum(dim=1).t() > lengths).t()
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mask.type(dtype or torch.bool)
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return mask
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def convert_to_non_torch_type(stats):
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"""Converts values in `stats` to non-Tensor numpy or python types.
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Args:
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stats (any): Any (possibly nested) struct, the values in which will be
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converted and returned as a new struct with all torch tensors
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being converted to numpy types.
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Returns:
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Any: A new struct with the same structure as `stats`, but with all
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values converted to non-torch Tensor types.
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"""
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# The mapping function used to numpyize torch Tensors.
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def mapping(item):
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if isinstance(item, torch.Tensor):
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return item.cpu().item() if len(item.size()) == 0 else \
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item.cpu().detach().numpy()
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else:
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return item
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return tree.map_structure(mapping, stats)
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def convert_to_torch_tensor(stats, device=None):
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"""Converts any struct to torch.Tensors.
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stats (any): Any (possibly nested) struct, the values in which will be
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converted and returned as a new struct with all leaves converted
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to torch tensors.
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Returns:
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Any: A new struct with the same structure as `stats`, but with all
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values converted to torch Tensor types.
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"""
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def mapping(item):
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if torch.is_tensor(item):
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return item if device is None else item.to(device)
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tensor = torch.from_numpy(np.asarray(item))
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# Floatify all float64 tensors.
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if tensor.dtype == torch.double:
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tensor = tensor.float()
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return tensor if device is None else tensor.to(device)
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return tree.map_structure(mapping, stats)
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def atanh(x):
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return 0.5 * torch.log((1 + x) / (1 - x))
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