コード例 #1
0
ファイル: dqn_zeus.py プロジェクト: qianrenjian/xingtian
    def create_model(self, model_info):
        """Create Deep-Q network."""
        model_config = model_info['model_config']
        zeus_model_name = model_config['zeus_model_name']

        model_cls = ClassFactory.get_cls(ClassType.SEARCH_SPACE,
                                         zeus_model_name)
        zeus_model = model_cls(state_dim=self.state_dim,
                               action_dim=self.action_dim)
        # zeus_model = DqnMlpNet(state_dim=self.state_dim, action_dim=self.action_dim)

        LossConfig.type = model_config['loss']
        OptimConfig.type = model_config['optim']
        OptimConfig.params.update({'lr': model_config['lr']})

        loss_input = dict()
        loss_input['inputs'] = [{
            "name": "input_state",
            "type": "float32",
            "shape": self.state_dim
        }]
        loss_input['labels'] = [{
            "name": "target_value",
            "type": "float32",
            "shape": self.action_dim
        }]
        model = Trainer(model=zeus_model,
                        backend='tensorflow',
                        device='GPU',
                        loss_input=loss_input,
                        lazy_build=False)
        return model
コード例 #2
0
ファイル: dqn_mlp_zeus.py プロジェクト: qianrenjian/xingtian
    def create_model(self, model_info):
        """Create Deep-Q network."""
        zeus_model = DqnMlpNet(state_dim=self.state_dim,
                               action_dim=self.action_dim)

        LossConfig.type = 'mse_loss'
        OptimConfig.type = 'Adam'
        OptimConfig.params.update({'lr': self.learning_rate})

        loss_input = dict()
        loss_input['inputs'] = [{
            "name": "input_state",
            "type": "float32",
            "shape": self.state_dim
        }]
        loss_input['labels'] = [{
            "name": "target_value",
            "type": "float32",
            "shape": self.action_dim
        }]

        model = Trainer(model=zeus_model,
                        backend='tensorflow',
                        device='GPU',
                        loss_input=loss_input,
                        lazy_build=False)
        return model
コード例 #3
0
    def create_model(self, model_info):
        zeus_model = PpoMlpNet(state_dim=self.state_dim, action_dim=self.action_dim)

        LossConfig.type = 'ppo_loss'
        OptimConfig.type = 'Adam'
        OptimConfig.params.update({'lr': LR})

        loss_input = dict()
        loss_input['inputs'] = [{"name": "input_state", "type": "float32", "shape": self.state_dim}]
        loss_input['labels'] = [{"name": "old_v", "type": "float32", "shape": 1}]
        loss_input['labels'].append({"name": "target_v", "type": "float32", "shape": 1})
        loss_input['labels'].append({"name": "old_p", "type": "float32", "shape": self.action_dim})
        loss_input['labels'].append({"name": "target_p", "type": "int32", "shape": 1})
        loss_input['labels'].append({"name": "adv", "type": "float32", "shape": 1})

        model = Trainer(model=zeus_model, lazy_build=False, loss_input=loss_input)
        return model
コード例 #4
0
    def create_model(self, model_info):
        """Create zeus network."""
        zeus_model = ImpalaMlpNet(state_dim=self.state_dim, action_dim=self.action_dim)

        LossConfig.type = 'impala_loss'
        OptimConfig.type = 'Adam'
        OptimConfig.params.update({'lr': LR})

        loss_input = dict()
        loss_input['inputs'] = [{"name": "input_state", "type": "float32", "shape": self.state_dim}]
        loss_input['labels'] = [{"name": "target_p", "type": "float32", "shape": self.action_dim}]
        loss_input['labels'].append({"name": "target_v", "type": "float32", "shape": 1})
        loss_input['labels'].append({"name": "adv", "type": "float32", "shape": 1})
        model = Trainer(model=zeus_model, backend='tensorflow', device='GPU',
                        loss_input=loss_input, lazy_build=False)

        return model