Looping over Sarsa algorithm for better Q values Let's say an RL trading system places trades based on pricing data.
Each episode represents 1 hour of trading, and there are 24 hours of data available. The Q table represents for a given state, what is the most action with the highest utility.
The state is a sequence of prices, and the action is either buy, hold, sell.
Instead of &quot;Loop for each episode&quot; as per the Sarsa algorithm :

I add an additional outer loop. Now instead of just looping for each episode we have:
for 1 to N:     
   &quot;Loop for each episode&quot;

Manually set N or exit out of the loop on convergence.
Is this the correct approach? Iterating multiple times over the episodes will produce more valuable state-action pairs in the Q table, because e greedy is not deterministic and for each iteration may exploit an action to greater reward than other episode epochs.
Given that your state space is continuous, then I would recommend using Deep Q-Learning. As you say, running several episodes will definitely be beneficial so that the agent is able to explore the space more thoroughly.