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What is the difference between Q-Learning and reinforcement learning?
The major difference between it and Q-Learning, is that the maximum reward for the next state is not necessarily used for updating the Q-values. Instead, a new action, and therefore reward, is selected using the same policy that determined the original action.
What is model-based learning?
Definition. Model-based learning is the formation and subsequent development of mental models by a learner. Most often used in the context of dynamic phenomena, mental models organize information about how the components of systems interact to produce the dynamic phenomena.
How is Q-learning different from Sarsa algorithm?
Q-Learning update equation. (Source: Reinforcement Learning: An Introduction by Sutton and Barto) Unlike the Sarsa algorithm, the Q-Learning update equation uses greedy-action selection for the next state to calculate the TD error.
Which is more complex expected sarsa or Sarsa update equation?
Expected Sarsa update equation. Given the next state Sₜ₊₁, this algorithm moves deterministically in the same direction as Sarsa moves in expectation, and hence, it is called Expected Sarsa. It is more computationally complex than Sarsa but in return, it eliminates the variance due to random selection of Aₜ₊₁.
How is the behaviour policy used in Sarsa?
Sarsa uses the behaviour policy (meaning, the policy used by the agent to generate experience in the environment, which is typically epsilon -greedy) to select an additional action At+1, and then uses Q (St+1, At+1) (discounted by gamma) as expected future returns in the computation of the update target.
What’s the difference between reinforcement learning and Sarsa?
According to the book Reinforcement Learning: An Introduction (by Sutton and Barto). In the SARSA algorithm, given a policy, the corresponding action-value function Q (in the state s and action a, at timestep t), i.e. Q (s t, a t ), can be updated as follows