Policy Improvement Archives

How does the Monte Carlo method estimate the value of a state or state-action pair in reinforcement learning?

Tuesday, 11 June 2024 by EITCA Academy

The Monte Carlo (MC) method is a fundamental approach in the field of reinforcement learning (RL) for estimating the value of states or state-action pairs. This method is particularly useful in model-free prediction and control, where the underlying dynamics of the environment are not known. The Monte Carlo method leverages the power of repeated random

Published in Artificial Intelligence, EITC/AI/ARL Advanced Reinforcement Learning, Prediction and control, Model-free prediction and control, Examination review

Tagged under: Artificial Intelligence, Model-Free Methods, Monte Carlo Methods, Policy Evaluation, Policy Improvement, Reinforcement Learning

What role does the policy play in determining the actions of an agent in a reinforcement learning scenario?

Monday, 13 May 2024 by EITCA Academy

In the domain of reinforcement learning (RL), a subfield of artificial intelligence, the policy plays a pivotal role in determining the actions of an agent within a given environment. To fully appreciate the significance and functionality of the policy, it is essential to consider the foundational concepts of reinforcement learning, explore the nature of policies,

Published in Artificial Intelligence, EITC/AI/ARL Advanced Reinforcement Learning, Introduction, Introduction to reinforcement learning, Examination review

Tagged under: Artificial Intelligence, Deterministic Policies, Markov Decision Processes, Policy Improvement, Reinforcement Learning, Stochastic Policies

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How does the Monte Carlo method estimate the value of a state or state-action pair in reinforcement learning?

What role does the policy play in determining the actions of an agent in a reinforcement learning scenario?