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How does Q function work?
Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. “Q” refers to the function that the algorithm computes – the expected rewards for an action taken in a given state.
What is Q-learning algorithm?
Q-learning is a model-free reinforcement learning algorithm. Q-learning is a values-based learning algorithm. Value based algorithms updates the value function based on an equation(particularly Bellman equation). Means it learns the value of the optimal policy independently of the agent’s actions.
Why did the Dyna agent with exploration bonus Dyna Q+ perform better in the first phase as well as in the second phase of the blocking and shortcut experiments?
Solution: The Dyna-Q+ agent performed better in the first phase because it did more exploration. The Dyna-Q+ agent did further exploration due to the exploration bonus, and found path improvement more quickly.
When does the Dyna-Q algorithm start learning?
The algorithm would not start learning until after you collected data, and you have no guidance available for how to efficiently explore the state and action space (because your learning algorithm has nothing to base a policy on). Dyna-Q allows the agent to start learning and improving incrementally much sooner.
What’s the difference between Dyna Q and Dyna AC?
Dyna-Q+ is Dyna-Q with an exploration bonus that encourages exploration.! Dyna-AC is a Dyna agent that uses an actor-critic learning method instead of Q- learning.! Planning and Learning 12
Why do we need Dyna-Q in reinforcement learning?
Dyna-Q allows the agent to start learning and improving incrementally much sooner. It does so at the expense of needing to work with rougher sample estimates of transition probabilities, and therefore needs a recency-weighted value update.
How does Dyna-Q learn about edge cases?
Dyna-Q will tend to focus on generating accurate values and an optimal policy only on reachable states from start state whilst following a near optimal policy, it may never learn about edge cases.