QLearningAgent.py

import gym
import numpy as np

# Step 2: Define the Q-Learning Agent
class QLearningAgent:
    def __init__(self, num_states, num_actions, learning_rate=0.1, discount_factor=0.9, exploration_prob=0.1):
        self.num_states = num_states
        self.num_actions = num_actions
        self.learning_rate = learning_rate
        self.discount_factor = discount_factor
        self.exploration_prob = exploration_prob
        self.q_table = np.zeros((num_states, num_actions))

    def choose_action(self, state):
        if np.random.uniform(0, 1) < self.exploration_prob:
            return np.random.randint(0, self.num_actions)
        else:
            return np.argmax(self.q_table[state])

    def update_q_table(self, state, action, reward, next_state):
        best_next_action = np.argmax(self.q_table[next_state])
        self.q_table[state, action] += self.learning_rate * (reward + self.discount_factor * self.q_table[next_state, best_next_action] - self.q_table[state, action])

# Step 4: Define Reward Function
def get_reward(action):
    # Define your custom reward logic based on the action taken
    # Return a numeric reward value
    pass

# Step 6: Interpret Acronyms
def interpret_acronym(acronym, agent):
    if acronym.upper() == "AI":
        # Assuming "AI" means to take a specific action in the environment
        state = env.reset()
        action = agent.choose_action(state)
        next_state, _, _, _ = env.step(action)
        reward = get_reward(action)
        agent.update_q_table(state, action, reward, next_state)

# Step 1: Set Up the Gym Environment
env = gym.make('CartPole-v1')
num_states = env.observation_space.shape[0]
num_actions = env.action_space.n

# Step 3: Initialize Q-Table
q_learning_agent = QLearningAgent(num_states, num_actions)

# Step 5: Implement Q-Learning Algorithm
for episode in range(1000):
    state = env.reset()
    done = False

    while not done:
        action = q_learning_agent.choose_action(state)
        next_state, _, done, _ = env.step(action)
        reward = get_reward(action)
        q_learning_agent.update_q_table(state, action, reward, next_state)
        state = next_state

# Step 6: Interpret Acronyms (Example Usage)
interpret_acronym("AI", q_learning_agent)