What is target network in DQN?
An important element of DQN is a target network, a technique introduced to stabilize learning. A target network is a copy of the action-value function (or Q-function) that is held constant to serve as a stable target for learning for some fixed number of timesteps.
Is DDQN better than DQN?
There is no thorough proof, theoretical or experimental that Double DQN is better then vanilla DQN. There are a lot of different tasks, paper and later experiments only explore some of them. What practitioner can take out of it is that on some tasks DDQN is better.
What is D3QN?
Duelling Double Deep Q Network (D3QN) controlling a simple hospital bed system. Duelling is very similar to Double DQN, except that the policy net splits into two. One component reduces to a single value, which will model the state value.
What is keras RL?
keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. You can use built-in Keras callbacks and metrics or define your own.
Why do deep Q networks overestimate Q values?
Maximization bias is the tendency of Deep Q Networks to overestimate both the value and the action-value (Q) functions. Why does it happen? Think that if for some reason the network overestimates a Q value for an action, that action will be chosen as the go-to action for the next step and the same overestimated value will be used as a target value.
Why do we use target network in iterative update?
We used an iterative update that adjusts the action-values (Q) towards target values that are only periodically updated, thereby reducing correlations with the target. So, in summary a target network required because the network keeps changing at each timestep and the “target values” are being updated at each timestep?
When does an a * overestimate the cost of a solution?
A pessimistic A* will always overestimate cost (e.g. “that option is probably pretty bad”). Once it has found a solution and it knows the true cost of the path, every other path will seem worse (because estimates are always worse than reality), and it will never try any alternative once the goal is found.
When does an overestimation function need to be optimistic?
With overestimation, A* has no idea when it can stop exploring a potential path as there can be paths with lower actual cost but higher estimated cost than the best currently known path to the goal. For A* to work correctly (always finding the ‘best’ solution, not just any), your estimation function needs to be optimistic.