#OFN Key Concepts of Reward Maximization
Agent and Environment Interaction:
The AI system (agent) interacts with its environment, receiving feedback in the form of rewards for its actions.
Example in #OpenfabricA I: A trading bot in a financial market takes actions (buy, sell, hold) and receives profits or losses as rewards.
Reward Function:
A function that maps each action taken in a given state to a numerical reward.
Example: In a recommendation engine, a reward could be assigned based on whether a user clicks on a suggested item or makes a purchase.
Cumulative Reward:
The goal is not to maximize immediate rewards but the total expected reward over time.
Formula:
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G_t is the total return starting from time step t,
γ (gamma) is the discount factor controlling the importance of future rewards.
Policy (π):
A strategy that defines the actions to take in each state to maximize rewards.
Example: A chatbot's policy determines how to respond to user inputs to keep users engaged and satisfied.
