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56 lines
1.7 KiB
56 lines
1.7 KiB
from typing import Dict, Optional, Union
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from ray.rllib.algorithms.dqn.dqn_torch_model import DQNTorchModel
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from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFC
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from ray.rllib.models.tf.fcnet import FullyConnectedNetwork
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from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
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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_utils import FLOAT_MIN, FLOAT_MAX
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torch, nn = try_import_torch()
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class TorchActionMaskModel(TorchModelV2, nn.Module):
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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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TorchModelV2.__init__(
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self, obs_space, action_space, num_outputs, model_config, name, **kwargs
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)
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nn.Module.__init__(self)
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self.count = 0
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self.internal_model = TorchFC(
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orig_space["data"],
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action_space,
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num_outputs,
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model_config,
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name + "_internal",
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)
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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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# Compute the unmasked logits.
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logits, _ = self.internal_model({"obs": input_dict["obs"]["data"]})
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action_mask = input_dict["obs"]["action_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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