CODEWITHSUNDEEP
pythontensorflowreinforcement-learningtf-agent
Mohammad.J
Mohammad.J

Reputation: 33

InvalidArgumentError: Must have updates.shape = indices.shape + params.shape[1:] or updates.shape = [] in TF-Agents

I have a problem with this simple code. I am looking for an experience replay code that is compatible with graph operations and TF Functions.


    import tensorflow as tf
    import numpy as np
    !pip install tf_agents
    import tf_agents 
    
    from tf_agents.replay_buffers import tf_uniform_replay_buffer
    data_spec = (
        tf.TensorSpec((5,), tf.float32, 'state'),
        tf.TensorSpec((), tf.float32, 'action')
        )
    print(data_spec)
    buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
        data_spec=data_spec,
        batch_size=1,
        max_length=1000
    )
    
    state = tf.constant(tf.ones(5), dtype=tf.float32)
    action = tf.constant(3.)
    save = (state, action)
    buffer.add_batch(save)

Error:

    InvalidArgumentError Traceback (most recent call last)
    in ()
    ----> 1 buffer.add_batch(save)
    
    6 frames
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py in raise_from_not_ok_status(e, name)
    7105 def raise_from_not_ok_status(e, name):
    7106 e.message += (" name: " + name if name is not None else "")
    -> 7107 raise core._status_to_exception(e) from None # pylint: disable=protected-access
    7108
    7109
    
    InvalidArgumentError: Must have updates.shape = indices.shape + params.shape[1:] or updates.shape = [], got updates.shape [5], indices.shape [1], params.shape [1000,5] [Op:ResourceScatterUpdate]

Upvotes: 1

Views: 329

Answers (0)

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