Monetary policy is more indirect.

But it still hasn't learned the optimal policy.

The monetary policy is a means to an end.

The bound is what does the optimal policy do?

That is, given a game, find the optimal policy.

The manufacturing sector is a frenzy over the new monetary policy.

So if it had learned the optimal policy, the policy loss would be zero, down here.

And from that, we can derive, look up, the optimal policy.

Monetary policy in United States has been characterized by tight credit over the months.

What does that mean? so that's the difference between the policy that the agent has learned and the optimal policy.

We can estimate the utility for some fixed policy, pi or we could estimate the Q values for the optimal policy while executing an exploration policy.

We're going to talk about game theory, which is the study of finding an optimal policy when that policy can depend on the opponent's policy and vice versa.

Excessive reliance on monetary policy an attempt to curb inflation could unnecessarily constrain credit and hence business.

So monetary policy, this is literally deciding how much money to print, so it's literally printing money.

THE MONETARY SYSTEM

Finally, I'd like to ask you what is the optimal policy for the parameters we just studied.

And no matter what policy we have, it's not going to be better than the optimal policy and so we can only be making things worse with our policy, not making them better.

Is it optimal?

Rather than telling you how to compute the optimal policy, which assigns an action to each of these cells,

The pi that we want the optimal policy is the one that's going to maximize the discounted, total Reward.

Is that Pareto optimal?

Is it absolutely optimal?

Monetary system Piano

Policy

A reinforcement learning agent can learn to explore the territory, find where the rewards are, and then learn an optimal policy.

Now, is that optimal? No!

It can be sub optimal.

IMF stands for International Monetary Fund.

later turned into a monetary system.

So, now that we have a value backup function that we discussed in depth, the question now becomes what's the optimal policy?

Now what that suggests is the design for an exploration agent that will be more proactive about exploring the world when it's uncertain, and will fall back to exploiting the optimal policy or whatever policy it has as close to optimal when it becomes more certain about the world.

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