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>
108 lines
3.3 KiB
Python
108 lines
3.3 KiB
Python
""" Code adapted from https://github.com/ikostrikov/pytorch-a3c"""
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import numpy as np
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from ray.rllib.utils import try_import_torch
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torch, nn = try_import_torch()
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def normc_initializer(std=1.0):
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def initializer(tensor):
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tensor.data.normal_(0, 1)
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tensor.data *= std / torch.sqrt(
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tensor.data.pow(2).sum(1, keepdim=True))
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return initializer
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def valid_padding(in_size, filter_size, stride_size):
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"""Note: Padding is added to match TF conv2d `same` padding. See
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www.tensorflow.org/versions/r0.12/api_docs/python/nn/convolution
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Params:
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in_size (tuple): Rows (Height), Column (Width) for input
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stride_size (tuple): Rows (Height), Column (Width) for stride
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filter_size (tuple): Rows (Height), Column (Width) for filter
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Output:
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padding (tuple): For input into torch.nn.ZeroPad2d
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output (tuple): Output shape after padding and convolution
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"""
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in_height, in_width = in_size
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filter_height, filter_width = filter_size
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stride_height, stride_width = stride_size
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out_height = np.ceil(float(in_height) / float(stride_height))
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out_width = np.ceil(float(in_width) / float(stride_width))
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pad_along_height = int(
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((out_height - 1) * stride_height + filter_height - in_height))
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pad_along_width = int(
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((out_width - 1) * stride_width + filter_width - in_width))
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pad_top = pad_along_height // 2
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pad_bottom = pad_along_height - pad_top
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pad_left = pad_along_width // 2
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pad_right = pad_along_width - pad_left
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padding = (pad_left, pad_right, pad_top, pad_bottom)
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output = (out_height, out_width)
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return padding, output
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class SlimConv2d(nn.Module):
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"""Simple mock of tf.slim Conv2d"""
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def __init__(
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self,
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in_channels,
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out_channels,
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kernel,
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stride,
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padding,
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# Defaulting these to nn.[..] will break soft torch import.
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initializer="default",
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activation_fn="default",
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bias_init=0):
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super(SlimConv2d, self).__init__()
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layers = []
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if padding:
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layers.append(nn.ZeroPad2d(padding))
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conv = nn.Conv2d(in_channels, out_channels, kernel, stride)
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if initializer:
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if initializer == "default":
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initializer = nn.init.xavier_uniform_
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initializer(conv.weight)
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nn.init.constant_(conv.bias, bias_init)
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layers.append(conv)
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if activation_fn:
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if activation_fn == "default":
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activation_fn = nn.ReLU
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layers.append(activation_fn())
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self._model = nn.Sequential(*layers)
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def forward(self, x):
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return self._model(x)
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class SlimFC(nn.Module):
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"""Simple PyTorch version of `linear` function"""
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def __init__(self,
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in_size,
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out_size,
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initializer=None,
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activation_fn=None,
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bias_init=0.0):
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super(SlimFC, self).__init__()
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layers = []
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linear = nn.Linear(in_size, out_size)
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if initializer:
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initializer(linear.weight)
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nn.init.constant_(linear.bias, bias_init)
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layers.append(linear)
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if activation_fn:
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layers.append(activation_fn())
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self._model = nn.Sequential(*layers)
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def forward(self, x):
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return self._model(x)
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