qlearning代码

Sure, here's an example of Q-learning code in Python:

import numpy as np

# Define the Q-learning
def q_learning(env, num_episodes, learning, discount_factor):
    # Initialize Q-table with zeros
    q_table =.zeros((env.num_states, env.num_actions    # Run Q-learning algorithm
    for episode range(num_episodes):
        state = env.reset()
 done = False

        while not done:
 # Choose action based on epsilon-greedy
            action = epsilon_greedy(q_table state, episode)

            Take action and observe the next state and reward
            next_state, reward, done = env.step(action)

            # Update Q-table using the Q-learning update rule
            q_value = q_table[state][action]
            max_q_value = np.max(q_table[next_state])
            q_table[state][action] += learning_rate * (reward + (discount_factor * max_q_value) - q_value)

            state = next_state

    return q_table

# Define epsilon-greedy policy
def epsilon_greedy(q_table, state, episode, epsilon=0.1):
    if np.random.uniform() < epsilon * (1 / (episode + 1)):
        # Explore by choosing a random action
        action = np.random.choice(len(q_table[state]))
    else:
        # Exploit by choosing the action with maximum Q-value
        action = np.argmax(q_table[state])

    return action

# Define the environment
class Environment:
    def __init__(self):
        self.num_states = 6
        self.num_actions = 2
        self.transitions = {
            0: {0: (2, -1), 1: (1, -1)},
            1: {0: (3, -1), 1: (2, -1)},
            2: {0: (4, -1), 1: (3, -1)},
            3: {0: (5, -1), 1: (4, -1)},
            4: {0: (5, 10), 1: (5, 0)},
            5: {0: (5, 0), 1: (5, 0)}
        }

    def reset(self):
        return 0

    def step(self, action):
        next_state, reward = self.transitions[state][action]
        done = (next_state == self.num_states - 1)
        return next_state, reward, done

# Run Q-learning
env = Environment()
q_table = q_learning(env, num_episodes=1000, learning_rate=0.1, discount_factor=0.9)

print("Q-table:")
print(q_table)

This code implements the Q-learning algorithm using an epsilon-greedy policy. The environment is a simple grid with 6 states and 2 actions. The Q-table is updated based on the observed rewards and transitions. After running the Q-learning algorithm, the final Q-table is printed. Feel free to modify the code to suit your specific problem.