def _build_for_dqn(self) -> Dict[str, Any]: """This isn't tuned.""" return { 'name': os.path.join(self.folder, 'DeepQAgent'), 'env_spec': self.env_spec, 'model_architecture': DenseNN(observation_shape=(2, ), n_actions=3, opt='adam', learning_rate=0.001, unit_scale=12, dueling=False), 'gamma': 0.99, 'final_reward': 650, 'replay_buffer_samples': 32, 'eps': EpsilonGreedy(eps_initial=0.1, decay=0.002, eps_min=0.002, actions_pool=list(range(3))), 'replay_buffer': ContinuousBuffer(buffer_size=200) }
def _build_for_dueling_dqn(self) -> Dict[str, Any]: config_dict = self._build_for_dqn() config_dict.update({'name': os.path.join(self.folder, 'DuelingDQN'), 'model_architecture': DenseNN(observation_shape=(4,), n_actions=2, opt='adam', learning_rate=0.0001, unit_scale=16, dueling=True)}) return config_dict
def _build_for_reinforce(self) -> Dict[str, Any]: return {'name': os.path.join(self.folder, 'REINFORCEAgent'), 'env_spec': self.env_spec, 'model_architecture': DenseNN(observation_shape=(4,), n_actions=2, opt='adam', unit_scale=16, output_activation='softmax', learning_rate=0.001, dueling=False), 'final_reward': -2, 'gamma': 0.99, 'alpha': 0.00001}
def _build_with_dense_model(dueling: bool = False) -> Dict[str, Any]: return {"model_architecture": DenseNN(observation_shape=(115,), n_actions=19, dueling=dueling, output_activation=None, opt='adam', learning_rate=0.000105)}