如何在批量學習中為 tf-agents 定義正確的形狀

Question

我正在嘗試使用 tf_agents 庫來訓練具有批量學習的 DDPG 代理。 但是，我需要定義一個觀察規范和動作規范，其中 state 代理將接收的張量的形狀。 我已經設法創建了可以提供數據的軌跡，但是這些軌跡和代理本身的形狀不匹配

我嘗試使用代理定義更改觀察和操作規范。 這是我的代理定義：

observation_spec = TensorSpec(shape = (1,),dtype =  tf.float32)
time_step_spec = time_step.time_step_spec(observation_spec)
action_spec = BoundedTensorSpec([1],tf.float32,minimum = -100, maximum = 100)
actor_network = ActorNetwork(
        input_tensor_spec=observation_spec,
        output_tensor_spec=action_spec,
        fc_layer_params=(100,200,100),
        name="ddpg_ActorNetwork"
    )
critic_net_input_specs = (observation_spec, action_spec)
critic_network = CriticNetwork(
    input_tensor_spec=critic_net_input_specs,
    observation_fc_layer_params=(200,100),
    joint_fc_layer_params=(100,200),
    action_fc_layer_params=None,
    name="ddpg_CriticNetwork"
)



agent = ddpg_agent.DdpgAgent(
    time_step_spec=time_step_spec,
    action_spec=action_spec,
    actor_network=actor_network,
    critic_network=critic_network,
    actor_optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
    critic_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
)

這就是軌跡的樣子

Trajectory(step_type=<tf.Variable 'Variable:0' shape=(1, 2) dtype=int32, numpy=array([[0, 1]], dtype=int32)>, observation=<tf.Variable 'Variable:0' shape=(1, 2) dtype=int32, numpy=array([[280, 280]], dtype=int32)>, action=<tf.Variable 'Variable:0' shape=(1, 2) dtype=float64, numpy=array([[nan,  0.]])>, policy_info=(), next_step_type=<tf.Variable 'Variable:0' shape=(1, 2) dtype=int32, numpy=array([[1, 1]], dtype=int32)>, reward=<tf.Variable 'Variable:0' shape=(1, 2) dtype=float64, numpy=array([[ -6.93147181, -12.14113521]])>, discount=<tf.Variable 'Variable:0' shape=(1, 2) dtype=float32, numpy=array([[0.9, 0.9]], dtype=float32)>)

我應該能夠調用 agent.train(trajectory) 並且它會工作但是我收到以下錯誤：

ValueError                                Traceback (most recent call last)
<ipython-input-325-bf162a5dc8d7> in <module>
----> 1 agent.train(trajs[0])

~/.local/lib/python3.7/site-packages/tf_agents/agents/tf_agent.py in train(self, experience, weights)
    213           "experience must be type Trajectory, saw type: %s" % type(experience))
    214 
--> 215     self._check_trajectory_dimensions(experience)
    216 
    217     if self._enable_functions:

~/.local/lib/python3.7/site-packages/tf_agents/agents/tf_agent.py in _check_trajectory_dimensions(self, experience)
    137     if not nest_utils.is_batched_nested_tensors(
    138         experience, self.collect_data_spec,
--> 139         num_outer_dims=self._num_outer_dims):
    140       debug_str_1 = tf.nest.map_structure(lambda tp: tp.shape, experience)
    141       debug_str_2 = tf.nest.map_structure(lambda spec: spec.shape,

~/.local/lib/python3.7/site-packages/tf_agents/utils/nest_utils.py in is_batched_nested_tensors(tensors, specs, num_outer_dims)
    142       'And spec_shapes:\n   %s' %
    143       (num_outer_dims, tf.nest.pack_sequence_as(tensors, tensor_shapes),
--> 144        tf.nest.pack_sequence_as(specs, spec_shapes)))
    145 
    146 

ValueError: Received a mix of batched and unbatched Tensors, or Tensors are not compatible with Specs.  num_outer_dims: 2.
Saw tensor_shapes:
   Trajectory(step_type=TensorShape([1, 2]), observation=TensorShape([1, 2]), action=TensorShape([1, 2]), policy_info=(), next_step_type=TensorShape([1, 2]), reward=TensorShape([1, 2]), discount=TensorShape([1, 2]))
And spec_shapes:
   Trajectory(step_type=TensorShape([]), observation=TensorShape([1]), action=TensorShape([1]), policy_info=(), next_step_type=TensorShape([]), reward=TensorShape([]), discount=TensorShape([]))

Answer 1

這可以通過使用環境輕松解決。 在 TF-Agents 中，環境需要遵循PyEnvironment class（然后用TFPyEnvironment包裝它以並行執行多個 env）。 如果你已經定義了你的環境來匹配這個類的規范，那么你的環境應該已經為你提供了兩個方法env.time_step_spec()和env.action_spec() 。 只需將這兩個提供給您的代理，您就應該完成。

如果您想從您的環境中獲得多個輸出，而不是所有 go 進入您的代理，它會變得有點復雜。 在這種情況下，您需要定義一個observation_and_action_constraint_splitter函數以傳遞給您的代理。 有關如何正確設置 TensorSpecs/ArraySpecs 的更多詳細信息，以及可行的示例，請參見我的回答here