Source Code For ENV generating Agent:

[Deadsg]

import gym import numpy as np

class SelfIteratingAgent: def init(self, num_actions, num_states, learning_rate=0.1, discount_factor=0.9, exploration_prob=0.2): self.num_actions = num_actions self.num_states = num_states 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.rand() < self.exploration_prob:
        return np.random.choice(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])
    target = reward + self.discount_factor * self.q_table[next_state, best_next_action]
    self.q_table[state, action] += self.learning_rate * (target - self.q_table[state, action])

Create the CartPole environment

env = gym.make('CartPole-v1') num_actions = env.action_space.n num_states = env.observation_space.shape[0]

agent = SelfIteratingAgent(num_actions, num_states)

Training loop

num_episodes = 1000

for episode in range(num_episodes): state = env.reset() done = False total_reward = 0

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

    # Modify the reward if needed
    # ...

    total_reward += reward

    # Update the Q-table
    agent.update_q_table(state, action, reward, next_state)

    state = next_state

print(f"Episode {episode}, Total Reward: {total_reward}")

env.close()