Q-learning Algorithm Implementation for Cliff Walking Problem

This script implements the Q-learning algorithm to demonstrate how SARSA (State-Action-Reward-State-Action) can solve the Cliff Walking problem. SARSA is an on-policy reinforcement learning algorithm that estimates action-value functions to determine optimal action selection strategies. In this problem scenario, the agent must navigate between cliff edges and safe paths while maximizing cumulative rewards. The implementation uses key components including: - Q-table initialization for state-action value storage - Epsilon-greedy policy for balancing exploration and exploitation - Temporal difference learning updates using the formula: Q(s,a) ← Q(s,a) + α[r + γQ(s',a') - Q(s,a)] - Episode-based training loops with terminal state handling Through this script, users can understand how SARSA algorithm applies to machine learning problems while gaining deeper insights into Q-learning implementation processes, including reward structuring, state transitions, and convergence monitoring for optimal policy derivation.