How the value function are updated?
Updating the value function is how the agent learns from past experiences, by updating the value of those states that have been through in the training process. State s’ is the next state of the current state s. We can update the value of the current state s by adding the differences in value between state s and s’.
What is RL policy evaluation?
Policy evaluation computes the value functions for a policy π using the Bellman equations. For example, Policy evaluation using Monte Carlo Rollouts. Monte Carlo plays out the whole episode until the end to calculate the total rewards.
What does a value function tell you about a policy?
A value function tells you, for a given policy, what the expected cumulative reward of taking action a in state s is. Forget about value iteration and policy iteration for a moment. The two things you should try to understand are policy evaluation and policy improvement.
How is the impact of a policy determined?
Policy impact evaluation can have multiple aims or purposes, including: Demonstrating the impact of the policy, by measuring changes in short- term, intermediate and long-term outcomes. Determining whether changes in outcomes can be attributed to the policy.
What do you need to know about policy evaluation?
The two things you should try to understand are policy evaluation and policy improvement. In policy evaluation, you figure out the state-value function for a given policy (which tells you your expected cumulative reward for being in a state and then acting according to the policy thereafter).
How are policy evaluation and policy extraction similar?
The algorithms for policy evaluation and finding optimal value function are highly similar except for a max operation (as highlighted) Similarly, the key step to policy improvement and policy extraction are identical except the former involves a stability check.