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https://github.com/vale981/ray
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68 lines
2.7 KiB
Python
68 lines
2.7 KiB
Python
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
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from ray.rllib.models.tf.fcnet import FullyConnectedNetwork as TFFCNet
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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 TorchFCNet
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from ray.rllib.utils.framework import try_import_tf, try_import_torch
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tf1, tf, tfv = try_import_tf()
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torch, nn = try_import_torch()
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class CustomTorchRPGModel(TorchModelV2, nn.Module):
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"""Example of interpreting repeated observations."""
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def __init__(self, obs_space, action_space, num_outputs, model_config,
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name):
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super().__init__(obs_space, action_space, num_outputs, model_config,
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name)
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nn.Module.__init__(self)
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self.model = TorchFCNet(obs_space, action_space, num_outputs,
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model_config, name)
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def forward(self, input_dict, state, seq_lens):
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# The unpacked input tensors, where M=MAX_PLAYERS, N=MAX_ITEMS:
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# {
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# 'items', <torch.Tensor shape=(?, M, N, 5)>,
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# 'location', <torch.Tensor shape=(?, M, 2)>,
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# 'status', <torch.Tensor shape=(?, M, 10)>,
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# }
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print("The unpacked input tensors:", input_dict["obs"])
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print()
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print("Unbatched repeat dim", input_dict["obs"].unbatch_repeat_dim())
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print()
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print("Fully unbatched", input_dict["obs"].unbatch_all())
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print()
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return self.model.forward(input_dict, state, seq_lens)
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def value_function(self):
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return self.model.value_function()
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class CustomTFRPGModel(TFModelV2):
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"""Example of interpreting repeated observations."""
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def __init__(self, obs_space, action_space, num_outputs, model_config,
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name):
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super().__init__(obs_space, action_space, num_outputs, model_config,
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name)
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self.model = TFFCNet(obs_space, action_space, num_outputs,
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model_config, name)
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def forward(self, input_dict, state, seq_lens):
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# The unpacked input tensors, where M=MAX_PLAYERS, N=MAX_ITEMS:
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# {
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# 'items', <tf.Tensor shape=(?, M, N, 5)>,
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# 'location', <tf.Tensor shape=(?, M, 2)>,
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# 'status', <tf.Tensor shape=(?, M, 10)>,
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# }
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print("The unpacked input tensors:", input_dict["obs"])
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print()
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print("Unbatched repeat dim", input_dict["obs"].unbatch_repeat_dim())
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print()
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if tf.executing_eagerly():
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print("Fully unbatched", input_dict["obs"].unbatch_all())
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print()
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return self.model.forward(input_dict, state, seq_lens)
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def value_function(self):
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return self.model.value_function()
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