Policy-Based Reinforcement Learning Algorithm

A Policy-Based Reinforcement Learning Algorithm is a reinforcement learning algorithm that directly learns the optimal policy (the policy that maximizes the expected reward).

• Context:
  • …
• Example(s):
  • a Policy-Gradient Policy-Based Algorithm.
  • PPO Reinforcement Learning.
  • …
• Counter-Example(s):
  • Value-Based Reinforcement Learning.
• See: Q-Learning Algorithm.

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References

2022

• https://chat.openai.com/chat
  • 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:

    1. 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.
    2. Representation: A policy-based algorithm represents the policy directly, while a value-based algorithm represents the value function.
    3. 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.
    4. 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.

2022

• https://chat.openai.com/chat
  • QUOTE: ... Policy gradient methods work by using gradient descent to update the parameters of the policy network. The gradients are calculated using the policy gradient theorem, which states that the gradient of the expected reward with respect to the policy parameters is equal to the expected gradient of the reward with respect to the actions taken, multiplied by the gradient of the actions taken with respect to the policy parameters. ...

2020

• https://towardsdatascience.com/policy-gradient-methods-104c783251e0
  • QUOTE: Policy-Gradient methods are a subclass of Policy-Based methods that estimate an optimal policy’s weights through gradient ascent.
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