determines how much the value of a state considers future states. A higher places more emphasis on distant future rewards, while helps algorithms converge by ensuring values do not diverge. 

controls the randomness of the agent's actions during policy search. A smaller results in more exploratory behavior, while a larger leads to more deterministic policies.  

determines the immediate reward only and ignores future states. A higher improves convergence by focusing solely on the present.

balances the trade-off between exploration and exploitation. A smaller focuses on exploring unknown states, while a larger exploits known rewards.