86 lines
2.9 KiB
86 lines
2.9 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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"""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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custom_config = model_config['custom_model_config']
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print(F"Original Space is: {orig_space}")
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#print(model_config)
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print(F"Observation space in model: {obs_space}")
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print(F"Provided action space in model {action_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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assert("shield" in custom_config)
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self.shield = custom_config["shield"]
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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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# disable action masking --> will likely lead to invalid actions
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self.no_masking = False
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if "no_masking" in model_config["custom_model_config"]:
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self.no_masking = model_config["custom_model_config"]["no_masking"]
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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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# print(F"Input dict is {input_dict} at obs: {input_dict['obs']}")
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# print(F"State is {state}")
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# print(input_dict["env"])
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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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# print(F"Caluclated Logits {logits} with size {logits.size()} Count: {self.count}")
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action_mask = input_dict["obs"]["avail_actions"]
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#print(F"Action mask is {action_mask} with dimension {action_mask.size()}")
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# If action masking is disabled, directly return unmasked logits
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if self.no_masking:
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return logits, state
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# assert(False)
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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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print(F"Infinity mask {inf_mask}, Masked logits {masked_logits}")
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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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