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What is Epsilon in Q-learning?
Epsilon is used when we are selecting specific actions base on the Q values we already have. As an example if we select pure greedy method ( epsilon = 0 ) then we are always selecting the highest q value among the all the q values for a specific state.
How do I reduce Epsilon Q-learning?
One way to fix this is to have a decreasing epsilon value, which starts off high at the beginning of the learning process (since the agent knows nothing about its environment, it is helpful for it to explore as much as possible in the beginning), and then decrease this epsilon based on the number of steps or episodes …
Is Q-Learning epsilon-greedy?
In DeepMind’s paper on Deep Q-Learning for Atari video games (here), they use an epsilon-greedy method for exploration during training. This means that when an action is selected in training, it is either chosen as the action with the highest q-value, or a random action.
When do you use Epsilon decay in exploration?
Epsilon Decay Epsilon is used when we are selecting specific actions base on the Q values we already have. As an example if we select pure greedy method (epsilon = 0) then we are always selecting the highest q value among the all the q values for a specific state. This causes issue in exploration as we can get stuck easily at a local optima.
When does learning rate decay in Epsilon greedy?
As the learning goes on both should decayed to stabilize and exploit the learned policy which converges to an optimal one. As the answer of Vishma Dias described learning rate [decay], I would like to elaborate the epsilon-greedy method that I think the question implicitly mentioned a decayed-epsilon-greedy method for exploration and exploitation.
Why does Q-learning use Epsilon greedy during testing?
The reason for using ϵ -greedy during testing is that, unlike in supervised machine learning (for example image classification), in reinforcement learning there is no unseen, held-out data set available for the test phase. This means the algorithm is tested on the very same setup that it has been trained on.
Why does Epsilon decay in a stochastic environment?
At the beginning, you want epsilon to be high so that you take big leaps and learn things. As you learn about future rewards, epsilon should decay so that you can exploit the higher Q-values you’ve found. However, does our learning rate also decay with time in a stochastic environment?