code: def test_deterministic_policy_gradient(self):
      self.start_tests(name="deterministic-policy-gradient")
  
      actions = dict(
          gaussian_action1=dict(
              type="float", shape=(1, 2), min_value=1.0, max_value=2.0
          ),
          gaussian_action2=dict(
              type="float", shape=(1,), min_value=-2.0, max_value=1.0
          ),
          beta_action=dict(
              type="float", shape=(), min_value=1.0, max_value=2.0
          ),
      )
      # TODO: no-RNN restriction can be removed
      policy = dict(
          network=dict(type="auto", size=8, depth=1, rnn=False),
          distributions=dict(
              gaussian_action2=dict(
                  type="gaussian", stddev_mode="global"
              ),
              beta_action="beta",
          ),
      )
      objective = "deterministic_policy_gradient"
      reward_estimation = dict(
          horizon=3,
          estimate_advantage=True,
          predict_horizon_values="late",
          predict_action_values=True,
          return_processing=dict(
              type="clipping", lower=-1.0, upper=1.0
          ),
          advantage_processing="batch_normalization",
      )
      baseline = dict(
          network=dict(type="auto", size=7, depth=1, rnn=False)
      )
      baseline_objective = "action_value"
      self.unittest(
          actions=actions,
          policy=policy,
          objective=objective,
          reward_estimation=reward_estimation,
          baseline=baseline,
          baseline_objective=baseline_objective,
      )
  
file_path: tensorforce/test/test_objectives.py
imports: import unittest
  from test.unittest_base import UnittestBase
label_desc: Use image node_id 1 for calling the TestObjectives obj's underlying member method code with example usage: obj.test_deterministic_policy_gradient() without return types