Machine learning in trading: theory, models, practice and algo-trading - page 2812
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This is when there are many agent states, for example in games. You only have 2-3 buy/sell states etc.
No, it's so primitive, otherwise it wouldn't be this direction at all.
state is not a buy/sell, buy/sell is an action, and state is roughly speaking the cluster number of the current environment, and each state cluster has its own action...
But theaction doesn't have to be primitive like buy/sell, it can be the agent's reasoning about the future for example ....
Like what if I buy now at [i], and on trade candle[i+1] the price will fall, but not below some price, I will wait for the next candle [i+2], but if the price goes even lower I will reverse, if not I will keep buy[i...20].
These are non-trivial reasoning about the future and lead to the discovery of conscious poziya....
But there are a myriad of such combinations of reasoning options, so that we don't have to go through them all, we train a Q function, that is, the agent takes for reasoning only those options that have a good Q value ,
Q neuron or matrix is trained beforehand...
This is how I see it...
The solution scheme is simple.
)))) yeah, sure...
I'm afraid of those who say "it's simple."
No, it's so primitive, otherwise it wouldn't be this direction at all...
state is not a by\sel, by\sel is an action, and state is roughly speaking the cluster number of the current environment, and each state cluster has its own action...
But theaction doesn't have to be primitive like a byte, it can be an agent's thoughts about the future for example ....
Like what if I buy at [i] now, and on trade candle[i+1] the price will fall, but not below some price, I will wait for the next candle [i+2], but if the price goes even lower I will reverse, if not I will keep buy[i...20].
These are non-trivial reasoning about the future and lead to the discovery of realised position....
But there are a myriad of such combinations of reasoning options, so that we do not have to go through them all, we train the Q function, that is, the agent takes for reasoning only those options that have a good Q value ,
Q neuron or matrix is trained beforehand...
This is how I see it...
I agree, buy sell no trade is not a state. There are a hulliard of states.))))))
I agree, buy sell not trade is not states. There are a hulliard of states.)))))
There aren't many states (if it's a cluster).
There are a horde of options for reasoning about future actions.
but reasoning is necessary to find the most correct actions in each state, moreover, they should be reviewed at each candle.
No, it's so primitive, otherwise it wouldn't be this direction at all...
state is not a by\sel, by\sel is an action, and state is roughly speaking the cluster number of the current environment, and each state cluster has its own action...
But theaction doesn't have to be primitive like a byte, it can be an agent's thoughts about the future for example ....
Like what if I buy at [i] now, and on trade candle[i+1] the price will fall, but not below some price, I will wait for the next candle [i+2], but if the price goes even lower I will reverse, if not I will keep buy[i...20].
These are non-trivial reasoning about the future and lead to the discovery of realised position....
But there are a myriad of such combinations of reasoning options, so that we do not have to go through them all, we train the Q function, that is, the agent takes for reasoning only those options that have a good Q value ,
Q neuron or matrix is trained beforehand...
This is how I see it...
When you start seeing it right, the wow factor goes away.
You're describing an agent's policy, a multi-pronged approach. I've written all about it. I'm writing in nerd language to make sense, and I've forgotten.
Exactly, it's so primitive.
There's one here who was foaming at the mouth about agents before she was banned.)
I agree, buy sell not trade is not states. There are a hulliard of states.))))
Agent states, or actions. I suggest you spend a couple of months reading books to understand what you have written about, and come to the same conclusions ) without the reaction of the environment to the agent's actions, there is nothing to optimise, it is done in one pass.