#StackBounty: #python #numpy #keras #keras-rl DQNAgent can't put batch size more than 1

Bounty: 50

When I try to train an agent with a batch_size greater than 1 it gives me an exception. Where is my issue?

lr = 1e-3
window_length = 1
emb_size = 10
look_back = 6

# "Expert" (regular dqn) model architecture

inp = Input(shape=(look_back,))
emb = Embedding(input_dim=env.action_space.n+1, output_dim = emb_size)(inp) 
rnn = Bidirectional(LSTM(5))(emb)
out = Dense(env.action_space.n, activation='softmax')(rnn)
expert_model = Model(inputs = inp, outputs = out)
expert_model.compile(loss='categorical_crossentropy', optimizer= Adam(lr))

# memory
memory = PrioritizedMemory(limit=1000000,  window_length=window_length)

# policy
policy = BoltzmannQPolicy()

# agent
dqn = DQNAgent(model=expert_model, nb_actions=env.action_space.n, policy=policy, memory=memory, 
               enable_double_dqn=False, enable_dueling_network=False, gamma=.9, batch_size = 100, #Here
               target_model_update=1e-2, processor = RecoProcessor())

Im printing some values directly from the code of keras-rl and it gives me this output:

State[array([0., 0., 0., 0., 0., 0.])]
Batch: [[[0. 0. 0. 0. 0. 0.]]]

But also this exception:

ValueError: Error when checking input: expected input_1 to have 2 dimensions, but got array with shape (1, 1, 6)

I could put the code of the processor class, and I think that there is the key of this, but first I want to make sure that there’s nothing wrong here.

Get this bounty!!!

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