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
How does the Bellman equation facilitate the process of policy evaluation in dynamic programming, and what role does the discount factor play in this context?
Tuesday, 11 June 2024
by EITCA Academy
The Bellman equation is a cornerstone in the field of dynamic programming and plays a pivotal role in the evaluation of policies within the framework of Markov Decision Processes (MDPs). In the context of reinforcement learning, the Bellman equation provides a recursive decomposition that simplifies the process of determining the value of a policy. This