You need to convert all the individual objects returned by both the training and validation generators to Numpy arrays:

    yield [np.array(imgs), np.array(cols)], np.array(targets)

Alternatively, a simpler and much more efficient solution is to not iterate over the data batch at all; instead, we can take advantage of the fact that these objects are already Numpy arrays when returned by ImageDataGenerator, so we can write:

    imgs = data[0]
    cols = data[1][:,:-1]
    targets = data[1][:,-1:]
    yield [imgs, cols], targets

You return four variables s1,s2,s3,s4 and receive them using a single variable obj. This is what is called a tuple, obj is associated with 4 values, the values of s1,s2,s3,s4. So, use index as you use in a list to get the value you want, in order.

obj=list_benefits()
print obj[0] + " is a benefit of functions!"
print obj[1] + " is a benefit of functions!"
print obj[2] + " is a benefit of functions!"
print obj[3] + " is a benefit of functions!"

The DqnAgent expects a TFPyEnvironment but you're implementing the environment as an PyEnvironment.

To fix this error you should convert the environment into the TensorFlow implementation before you are creating the agent.

You can do it this way:

from tf_agents.environments import tf_py_environment

train_env = myEnv()

train_env_tf = tf_py_environment.TFPyEnvironment(train_envt)

q_net = q_network.QNetwork(
    train_env_tf.observation_spec(),
    train_env_tf.action_spec(),
    fc_layer_params=(10,))

optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)
train_step_counter = tf.Variable(0)

agent = dqn_agent.DqnAgent(
    train_env_tf.time_step_spec(),
    train_env_tf.action_spec(),
    q_network=q_net,
    optimizer=optimizer,
    td_errors_loss_fn=common.element_wise_squared_loss,
    train_step_counter=train_step_counter)