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Why is the Q-loss not converging for DQN?
The Q-loss is calculated as MSE. Do you have ideas why the Q-loss is not converging? Does the Q-Loss have to converge for DQN algorithm? I’m wondering, why Q-loss is not discussed in most of the papers. Yes, the loss must coverage, because of the loss value means the difference between expected Q value and current Q value.
When is the Q loss not converging in DeepMind?
In Deepmind’s 2015 DQN, the author clipped the gradient by limiting the value within [-1, 1]. In the other case, the author of Prioritized Experience Replay clip gradient by limiting the norm within 10. Here’re the examples: I think it’s normal that the Q-loss is not converging as your data keeps changing when your policy updates.
How is the DQN algorithm used in TensorFlow?
I’m using the DQN algorithm to train an agent in my environment, that looks like this: Rewards: -100 for crashing into other cars, positive reward according to the absolute difference to the desired speed (+50 if driving at desired speed)
What is the Q value of selecting action a?
The above equation states that the Q Value yielded from being at state s and selecting action a, is the immediate reward received, r (s,a), plus the highest Q Value possible from state s’ (which is the state we ended up in after taking action a from state s ).
How is reinforcement learning done in deep Q networks?
The way it is done is by giving the Agent rewards or punishments based on the actions it has performed on different scenarios. One of the first practical Reinforcement Learning methods I learned was Deep Q Networks, and I believe it’s an excellent kickstart to this journey.
Why is Q referred to as Q-target?
This is why Q (s’,a; θ) is usually referred to as Q-target. Moving on: Training. In Reinforcement Learning, the training set is created as we go; we ask the Agent to try and select the best action using the current network — and we record the state, action, reward and the next state it ended up at.
Why is the Q-loss not converging in TensorFlow?
However, for all different settings of hyperparameter the Q-loss is not converging (see figure 2 ). I assume, that the lacking convergence of the Q-loss might be the limiting factor for better results. I’m using a target network which is updated every 20k timesteps. The Q-loss is calculated as MSE.
Why does regular Q learning ( and DQN ) overestimate the Q values?
The motivation for the introduction of double DQN (and double Q-learning) is that the regular Q-learning (or DQN) can overestimate the Q value, but is there a brief explanation as to why it is overestimated? The overestimation comes from the random initialisation of your Q-value estimates.
Why is Q not converging with Stack Overflow?
Generating the targets using the older set of parameters adds a delay between the time an update to Q is made and the time the update affects the targets y j, making divergence or oscillations much more unlikely.
When to clip gradient in DeepMind 2015 DQN?
If the divergence of loss value is caused by gradient explode, you can clip the gradient. In Deepmind’s 2015 DQN, the author clipped the gradient by limiting the value within [-1, 1]. In the other case, the author of Prioritized Experience Replay clip gradient by limiting the norm within 10.