With a model, memory, and policy defined, we're now ready to create a deep Q network Agent and send that agent those objects. Keras RL provides an agent class called rl.agents.dqn.DQNAgent that we can use for this, as shown in the following code:
dqn = DQNAgent(model=model, nb_actions=num_actions, memory=memory, nb_steps_warmup=10, target_model_update=1e-2, policy=policy)dqn.compile(Adam(lr=1e-3), metrics=['mae'])
Two of these parameters are probably unfamiliar at this point, target_model_update and nb_steps_warmup:
- nb_steps_warmup: Determines how long we wait before we start doing experience replay, which if you recall, is when we actually start training the network. This lets us build up enough experience to build a proper minibatch. ...
