TF-agents - 重播缓冲区将轨迹添加到批次形状不匹配

Question

I'm posting a question that was posted by another user and then deleted. I had the same question, and I found an answer. The original question:

I am currently trying to implement a categorical DQN following this tutorial: https://www.tensorflow.org/agents/tutorials/9_c51_tutorial

The following part is giving me a bit of a headache though:

random_policy = random_tf_policy.RandomTFPolicy(env.time_step_spec(),
                                                env.action_spec())

replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=agent.collect_data_spec,
batch_size=1,
max_length=replay_buffer_capacity) # this is 100

# ...

def collect_step(environment, policy):
  time_step = environment.current_time_step()
  action_step = policy.action(time_step)
  next_time_step = environment.step(action_step.action)
  traj = trajectory.from_transition(time_step, action_step, next_time_step)
  print(traj)

  # Add trajectory to the replay buffer
  replay_buffer.add_batch(traj)

for _ in range(initial_collect_steps):
  collect_step(env, random_policy)

For context: agent.collect_data_spec is of the following shape:

Trajectory(step_type=TensorSpec(shape=(), dtype=tf.int32, name='step_type'), observation=BoundedTensorSpec(shape=(4, 84, 84), dtype=tf.float32, name='screen', minimum=array(0., dtype=float32), maximum=array(1., dtype=float32)), action=BoundedTensorSpec(shape=(), dtype=tf.int32, name='play', minimum=array(0), maximum=array(6)), policy_info=(), next_step_type=TensorSpec(shape=(), dtype=tf.int32, name='step_type'), reward=TensorSpec(shape=(), dtype=tf.float32, name='reward'), discount=BoundedTensorSpec(shape=(), dtype=tf.float32, name='discount', minimum=array(0., dtype=float32), maximum=array(1., dtype=float32)))

And here is what a sample traj looks like:

Trajectory(step_type=<tf.Tensor: shape=(), dtype=int32, numpy=0>, observation=<tf.Tensor: shape=(4, 84, 84), dtype=float32, numpy=array([tensor contents omitted], dtype=float32)>, action=<tf.Tensor: shape=(), dtype=int32, numpy=1>, policy_info=(), next_step_type=<tf.Tensor: shape=(), dtype=int32, numpy=1>, reward=<tf.Tensor: shape=(), dtype=float32, numpy=0.0>, discount=<tf.Tensor: shape=(), dtype=float32, numpy=1.0>)

So, everything should check out, right? The environment outputs a tensor of shape [4, 84, 84], same as the replay buffer expects. Except I'm getting the following error:

tensorflow.python.framework.errors_impl.InvalidArgumentError: Must have updates.shape = indices.shape + params.shape[1:] or updates.shape = [], got updates.shape [4,84,84], indices.shape [1], params.shape [100,4,84,84] [Op:ResourceScatterUpdate]

Which suggests that it is actually expecting a tensor of shape [1, 4, 84, 84] . The thing is though, if I have my environment output a tensor of that shape, I then get another error message telling me that the output shape doesn't match the spec shape (duh). And if I then adjust the spec shape to be [1, 4, 84, 84] , suddenly the replay buffer expects a shape of [1, 1, 4, 84, 84] , and so on...

Finally, for completion, here you have the time_step_spec and action_spec of my environment respectively:

TimeStep(step_type=TensorSpec(shape=(), dtype=tf.int32, name='step_type'), reward=TensorSpec(shape=(), dtype=tf.float32, name='reward'), discount=BoundedTensorSpec(shape=(), dtype=tf.float32, name='discount', minimum=array(0., dtype=float32), maximum=array(1., dtype=float32)), observation=BoundedTensorSpec(shape=(4, 84, 84), dtype=tf.float32, name='screen', minimum=array(0., dtype=float32), maximum=array(1., dtype=float32)))
---
BoundedTensorSpec(shape=(), dtype=tf.int32, name='play', minimum=array(0), maximum=array(6))

I've tried pretty much the better half of today trying to get the tensor to fit properly, but you cannot reshape it since it's an attribute so in a last ditch effort I'm hoping maybe some kind stranger out there can tell me what the heck is going on here.

Thank you in advance!

Answer 1

It seems that in the collect_step function, traj is aa single trajectory, not a batch. Therefore you need to expand the dimensions into a batch and then use it. Note that you can't just do tf.expand_dims(traj, 0) . There's a helper function for doing it for nested structures.

def collect_step(environment, policy):
    time_step = environment.current_time_step()
    action_step = policy.action(time_step)
    next_time_step = environment.step(action_step.action)
    traj = trajectory.from_transition(time_step, action_step, next_time_step)
    batch = tf.nest.map_structure(lambda t: tf.expand_dims(t, 0), traj)
    # Add trajectory to the replay buffer
    replay_buffer.add_batch(batch)