So we can decompose Q(s,a) as the sum of: Remember that Q-values correspond to how good it is to be at that state and taking an action at that state Q(s,a). Implementation Dueling DQN (aka DDQN) Theory Therefore, Double DQN helps us reduce the overestimation of q values and, as a consequence, helps us train faster and have more stable learning.
#Kairosoft games high replay value update
We update the target network with the DQNetwork every tau step ( tau is an hyper-parameter that we define).ĭouble DQNs, or double Learning, was introduced by Hado van Hasselt. Finally, during the training, we calculate the TD target using our target network.Then, we create a function that will take our DQNetwork parameters and copy them to our TargetNetwork.First, we create two networks ( DQNetwork, TargetNetwork).Implementing fixed q-targets is pretty straightforward: Thanks to this procedure, we’ll have more stable learning because the target function stays fixed for a while. At every Tau step, we copy the parameters from our DQN network to update the target network.Using a separate network with a fixed parameter (let’s call it w-) for estimating the TD target.Instead, we can use the idea of fixed Q-targets introduced by DeepMind: This leads to a very strange path of chasing (a big oscillation in training). It’s like if you were a cowboy (the Q estimation) and you want to catch the cow (the Q-target) you must get closer (reduce the error).Īt each time step, you’re trying to approach the cow, which also moves at each time step (because you use the same parameters). It’s like chasing a moving target! This lead to a big oscillation in training. So, we’re getting closer to our target but the target is also moving. Therefore, it means that at every step of training, our Q values shift but also the target value shifts. As a consequence, there is a big correlation between the TD target and the parameters (w) we are changing. However, the problem is that we using the same parameters (weights) for estimating the target and the Q value.
#Kairosoft games high replay value plus
Using the Bellman equation, we saw that the TD target is just the reward of taking that action at that state plus the discounted highest Q value for the next state. We saw in the Deep Q Learning article that, when we want to calculate the TD error (aka the loss), we calculate the difference between the TD target (Q_target) and the current Q value (estimation of Q).īut we don’t have any idea of the real TD target. Our AI must navigate towards the fundamental goal (the vest), and make sure they survive at the same time by killing enemies. We’ll implement an agent that learns to play Doom Deadly corridor. Prioritized Experience Replay (aka PER).So, today we’ll see four strategies that improve - dramatically - the training and the results of our DQN agents: Since then, a lot of improvements have been made. However, during the training, we saw that there was a lot of variability.ĭeep Q-Learning was introduced in 2014. In the video version, we trained a DQN agent that plays Space invaders. In our last article about Deep Q Learning with Tensorflow, we implemented an agent that learns to play a simple version of Doom. By Thomas Simonini Improvements in Deep Q Learning: Dueling Double DQN, Prioritized Experience Replay, and fixed Q-targets This article is part of Deep Reinforcement Learning Course with Tensorflow ?️.