'Reinforcement learning' means:

Reinforced Learning components include: • Agent (decision maker) • Environment (what the agent interacts with) • State (current situation) • Action (agent's choice) • Reward (feedback signal) • Policy (agent's strategy) Algorithms include Q-Learning, Deep Q-Network (DQN), Policy Gradient, A3C.  Reinforcement Learning Banking examples include Algorithmic trading (agent learns to buy/sell stocks to maximize portfolio return), Dynamic loan pricing (agent adjusts interest rate offers based on customer behaviour), ATM cash replenishment optimization (agent decides optimal cash loading schedule to minimize both shortfall and excess) and Personalized financial product recommendation systems.