C:\Python\Python37\lib\site-packages\keras_rl-0.4.2-py3.7.egg\rl\agents\dqn.py in __init__(self, model, policy, test_policy, enable_double_dqn, enable_dueling_network, dueling_type, *args, **kwargs)

[Shravan Suthar]

C:\Python\Python37\lib\site-packages\keras_rl-0.4.2-py3.7.egg\rl\agents\dqn.py in init(self, model, policy, test_policy, enable_double_dqn, enable_dueling_network, dueling_type, *args, **kwargs)
106
107 # Validate (important) input.
--> 108 if hasattr(model.output, 'len') and len(model.output) > 1:
109 raise ValueError('Model "{}" has more than one output. DQN expects a model that has a single output.'.format(model))
110 if model.output._keras_shape != (None, self.nb_actions):

C:\Python\Python37\lib\site-packages\tensorflow\python\keras\engine\keras_tensor.py in len(self)
238
239 def len(self):
--> 240 raise TypeError('Keras symbolic inputs/outputs do not '
241 'implement __len__. You may be '
242 'trying to pass Keras symbolic inputs/outputs '

TypeError: Keras symbolic inputs/outputs do not implement __len__. You may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model. This error will also get raised if you try asserting a symbolic input/output directly.

please help me .....

Originally posted by @Shravansuthar211 in #348 (comment)

[Vadim Liventsev]

I have the same issue. Minimal script to reproduce it:

import gym
import click

from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.optimizers import Adam

from rl.agents.dqn import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory

def train(env):
    nb_actions = env.action_space.n

    # Next, we build a very simple model.
    model = Sequential()
    model.add(Flatten(input_shape=(1,) + env.observation_space.shape))
    model.add(Dense(16))
    model.add(Activation('relu'))
    model.add(Dense(16))
    model.add(Activation('relu'))
    model.add(Dense(16))
    model.add(Activation('relu'))
    model.add(Dense(nb_actions))
    model.add(Activation('linear'))
    print(model.summary())

    # Finally, we configure and compile our agent. You can use every built-in Keras optimizer and
    # even the metrics!
    memory = SequentialMemory(limit=50000, window_length=1)
    policy = BoltzmannQPolicy()
    dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=10,
                target_model_update=1e-2, policy=policy)
    dqn.compile(Adam(lr=1e-3), metrics=['mae'])

    # Okay, now it's time to learn something! We visualize the training here for show, but this
    # slows down training quite a lot. You can always safely abort the training prematurely using
    # Ctrl + C.
    dqn.fit(env, nb_steps=50000, visualize=True, verbose=2)
    return dqn

@click.command()
@click.argument('env_name')
def main(env_name):
    env = gym.make(env_name)
    dqn = train(env)

    # After training is done, we save the final weights.
    dqn.save_weights('dqn_weights.h5f', overwrite=True)

    # Finally, evaluate our algorithm for 5 episodes.
    dqn.test(env, nb_episodes=5, visualize=True)

if __name__ == '__main__':
    main()

Environment:

click==7.1.2
tensorflow==2.4.1
keras-rl==0.4.2
gym==0.17.3

Error:

Traceback (most recent call last):
  File "keras_rl_solve.py", line 67, in <module>
    main()
  File "/home/vadim0x60/.pyenv/versions/3.8.6/lib/python3.8/site-packages/click/core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "/home/vadim0x60/.pyenv/versions/3.8.6/lib/python3.8/site-packages/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/home/vadim0x60/.pyenv/versions/3.8.6/lib/python3.8/site-packages/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/home/vadim0x60/.pyenv/versions/3.8.6/lib/python3.8/site-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "keras_rl_solve.py", line 58, in main
    dqn = train(env)
  File "keras_rl_solve.py", line 35, in train
    dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=10,
  File "/home/vadim0x60/.pyenv/versions/3.8.6/lib/python3.8/site-packages/rl/agents/dqn.py", line 108, in __init__
    if hasattr(model.output, '__len__') and len(model.output) > 1:
  File "/home/vadim0x60/.pyenv/versions/3.8.6/lib/python3.8/site-packages/tensorflow/python/keras/engine/keras_tensor.py", line 240, in __len__
    raise TypeError('Keras symbolic inputs/outputs do not '
TypeError: Keras symbolic inputs/outputs do not implement `__len__`. You may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model. This error will also get raised if you try asserting a symbolic input/output directly.

[Vadim Liventsev]

I tried CartPole-v0, MountainCar-v1 and Taxi-v3

[stale]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

[Michael Poehlmann]

I have the same issue. It occurs when running examples/dqn_cartpole.py.