policy-based algorithm: Difference between revisions

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#REDIRECT [[Policy-Based Reinforcement Learning Algorithm]]
 
 
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** QUESTION: what is a policy-based algorithm and how does it differ from a value-based algorithm?
** ANSWER:
 
In reinforcement learning, a policy-based algorithm is a type of algorithm that directly learns a policy, which is a mapping from states to actions. The goal of a policy-based algorithm is to learn the optimal policy, which is the policy that maximizes the expected reward.
 
On the other hand, a value-based algorithm is a type of algorithm that learns a value function, which estimates the expected reward for a given state or state-action pair. The goal of a value-based algorithm is to learn the optimal value function, which is the function that estimates the maximum expected reward for a given state or state-action pair.
 
There are several key differences between policy-based and value-based algorithms:
 
# Objectives: As mentioned above, the objective of a policy-based algorithm is to learn the optimal policy, while the objective of a value-based algorithm is to learn the optimal value function.
# Representation: A policy-based algorithm represents the policy directly, while a value-based algorithm represents the value function.
# Estimation: A policy-based algorithm estimates the value of a policy directly, while a value-based algorithm estimates the value of a state or state-action pair by backing up the value estimates through the value function.
# Exploration: Policy-based algorithms typically rely on exploration strategies to improve the policy, while value-based algorithms typically rely on exploration strategies to improve the value function estimates.
 
Overall, policy-based and value-based algorithms are two different approaches to solving reinforcement learning problems. Both approaches have their own strengths and weaknesses, and which one is best depends on the specific problem being solved.
 
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Latest revision as of 21:09, 18 December 2022

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