mirror of
https://github.com/vale981/ray
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100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
from gym.spaces import Dict
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from ray.rllib.models.tf.fcnet import FullyConnectedNetwork
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from ray.rllib.models.tf.tf_modelv2 import TFModelV2
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from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
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from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFC
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from ray.rllib.utils.framework import try_import_tf, try_import_torch
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from ray.rllib.utils.torch_ops import FLOAT_MIN
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tf1, tf, tfv = try_import_tf()
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torch, nn = try_import_torch()
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class ActionMaskModel(TFModelV2):
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"""Model that handles simple discrete action masking.
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This assumes the outputs are logits for a single Categorical action dist.
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Getting this to work with a more complex output (e.g., if the action space
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is a tuple of several distributions) is also possible but left as an
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exercise to the reader.
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"""
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def __init__(self, obs_space, action_space, num_outputs, model_config,
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name, **kwargs):
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orig_space = getattr(obs_space, "original_space", obs_space)
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assert isinstance(orig_space, Dict) and \
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"action_mask" in orig_space.spaces and \
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"observations" in orig_space.spaces
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super().__init__(obs_space, action_space, num_outputs, model_config,
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name)
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self.internal_model = FullyConnectedNetwork(
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orig_space["observations"], action_space, num_outputs,
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model_config, name + "_internal")
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def forward(self, input_dict, state, seq_lens):
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# Extract the available actions tensor from the observation.
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action_mask = input_dict["obs"]["action_mask"]
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# Compute the unmasked logits.
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logits, _ = self.internal_model({
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"obs": input_dict["obs"]["observations"]
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})
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# Convert action_mask into a [0.0 || -inf]-type mask.
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inf_mask = tf.maximum(tf.math.log(action_mask), tf.float32.min)
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masked_logits = logits + inf_mask
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# Return masked logits.
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return masked_logits, state
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def value_function(self):
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return self.internal_model.value_function()
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class TorchActionMaskModel(TorchModelV2, nn.Module):
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"""PyTorch version of above ActionMaskingModel."""
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def __init__(
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self,
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obs_space,
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action_space,
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num_outputs,
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model_config,
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name,
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**kwargs,
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):
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orig_space = getattr(obs_space, "original_space", obs_space)
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assert isinstance(orig_space, Dict) and \
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"action_mask" in orig_space.spaces and \
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"observations" in orig_space.spaces
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TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
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model_config, name, **kwargs)
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nn.Module.__init__(self)
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self.internal_model = TorchFC(orig_space["observations"], action_space,
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num_outputs, model_config,
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name + "_internal")
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def forward(self, input_dict, state, seq_lens):
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# Extract the available actions tensor from the observation.
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action_mask = input_dict["obs"]["action_mask"]
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# Compute the unmasked logits.
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logits, _ = self.internal_model({
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"obs": input_dict["obs"]["observations"]
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})
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# Convert action_mask into a [0.0 || -inf]-type mask.
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inf_mask = torch.clamp(torch.log(action_mask), min=FLOAT_MIN)
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masked_logits = logits + inf_mask
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# Return masked logits.
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return masked_logits, state
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def value_function(self):
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return self.internal_model.value_function()
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