qlearning_gym_3.py

import gym
import numpy as np
import matplotlib.pyplot as plt

env = gym.make("MountainCar-v0")
env.reset()

LEARNING_RATE = 0.1
# discount is weight
# measure of how important we find future actions
# how much we value future reward over current reward
DISCOUNT = 0.95
EPISODES = 20000
SHOW_EVERY = 500
STATS_EVERY = 100

DISCRETE_OS_SIZE = [20] * len(env.observation_space.high)
discrete_os_win_size = (env.observation_space.high - env.observation_space.low) / DISCRETE_OS_SIZE

# chance of doing random action
epsilon = 1.0  # not a constant, qoing to be decayed
START_EPSILON_DECAYING = 1
END_EPSILON_DECAYING = EPISODES // 2
# amount to decay each episode
epsilon_decay_value = epsilon / (END_EPSILON_DECAYING - START_EPSILON_DECAYING)

q_table = np.random.uniform(low=-2, high=0, size=(DISCRETE_OS_SIZE + [env.action_space.n]))

ep_rewards = []
aggr_ep_rewards = {'ep': [], 'avg': [], 'max': [], 'min': []}


def get_discrete_state(state):
    discrete_state = (state - env.observation_space.low)/discrete_os_win_size
    # we use this tuple to look up the 3 Q values for the available actions in the q-table
    return tuple(discrete_state.astype(np.int))


for episode in range(EPISODES):
    episode_reward = 0
    discrete_state = get_discrete_state(env.reset())
    done = False

    if episode % SHOW_EVERY == 0:
        render = True
        print(episode)
    else:
        render = False

    while not done:

        if np.random.random() > epsilon:
            # Get action from Q table
            action = np.argmax(q_table[discrete_state])
        else:
            # Get random action
            action = np.random.randint(0, env.action_space.n)

        new_state, reward, done, _ = env.step(action)
        episode_reward += reward

        new_discrete_state = get_discrete_state(new_state)

        if render:
            env.render()

        if not done:
            # Maximum possible Q value in next step (for new state)
            max_future_q = np.max(q_table[new_discrete_state])

            # Current Q value (for current state and performed action)
            current_q = q_table[discrete_state + (action,)]

            # equation for a new Q value for current state and action
            new_q = (1 - LEARNING_RATE) * current_q + \
                LEARNING_RATE * (reward + DISCOUNT * max_future_q)

            # Update Q table with new Q value
            q_table[discrete_state + (action,)] = new_q

        # Simulation ended (for any reson) - if goal position is achived - update Q value with reward directly
        elif new_state[0] >= env.goal_position:
            print(f'Made it on episode {episode}')
            q_table[discrete_state + (action,)] = 0

        discrete_sate = new_discrete_state

    if END_EPSILON_DECAYING >= episode >= START_EPSILON_DECAYING:
        epsilon -= epsilon_decay_value

    ep_rewards.append(episode_reward)

    if not episode % STATS_EVERY:
        #np.save(f"qtables/{episode}-qtable.npy", q_table)

        average_reward = sum(ep_rewards[-STATS_EVERY:])/STATS_EVERY
        aggr_ep_rewards['ep'].append(episode)
        aggr_ep_rewards['avg'].append(average_reward)
        aggr_ep_rewards['max'].append(max(ep_rewards[-STATS_EVERY:]))
        aggr_ep_rewards['min'].append(min(ep_rewards[-STATS_EVERY:]))

env.close()

plt.plot(aggr_ep_rewards['ep'], aggr_ep_rewards['avg'], label="average rewards")
plt.plot(aggr_ep_rewards['ep'], aggr_ep_rewards['max'], label="max rewards")
plt.plot(aggr_ep_rewards['ep'], aggr_ep_rewards['min'], label="min rewards")
plt.legend(loc=4)
plt.show()