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February 22, 2019 08:09
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Use Q-learning to solve the OpenAI Gym Mountain Car problem
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import numpy as np | |
import gym | |
import matplotlib.pyplot as plt | |
# Import and initialize Mountain Car Environment | |
env = gym.make('MountainCar-v0') | |
env.reset() | |
# Define Q-learning function | |
def QLearning(env, learning, discount, epsilon, min_eps, episodes): | |
# Determine size of discretized state space | |
num_states = (env.observation_space.high - env.observation_space.low)*\ | |
np.array([10, 100]) | |
num_states = np.round(num_states, 0).astype(int) + 1 | |
# Initialize Q table | |
Q = np.random.uniform(low = -1, high = 1, | |
size = (num_states[0], num_states[1], | |
env.action_space.n)) | |
# Initialize variables to track rewards | |
reward_list = [] | |
ave_reward_list = [] | |
# Calculate episodic reduction in epsilon | |
reduction = (epsilon - min_eps)/episodes | |
# Run Q learning algorithm | |
for i in range(episodes): | |
# Initialize parameters | |
done = False | |
tot_reward, reward = 0,0 | |
state = env.reset() | |
# Discretize state | |
state_adj = (state - env.observation_space.low)*np.array([10, 100]) | |
state_adj = np.round(state_adj, 0).astype(int) | |
while done != True: | |
# Render environment for last five episodes | |
if i >= (episodes - 20): | |
env.render() | |
# Determine next action - epsilon greedy strategy | |
if np.random.random() < 1 - epsilon: | |
action = np.argmax(Q[state_adj[0], state_adj[1]]) | |
else: | |
action = np.random.randint(0, env.action_space.n) | |
# Get next state and reward | |
state2, reward, done, info = env.step(action) | |
# Discretize state2 | |
state2_adj = (state2 - env.observation_space.low)*np.array([10, 100]) | |
state2_adj = np.round(state2_adj, 0).astype(int) | |
#Allow for terminal states | |
if done and state2[0] >= 0.5: | |
Q[state_adj[0], state_adj[1], action] = reward | |
# Adjust Q value for current state | |
else: | |
delta = learning*(reward + | |
discount*np.max(Q[state2_adj[0], | |
state2_adj[1]]) - | |
Q[state_adj[0], state_adj[1],action]) | |
Q[state_adj[0], state_adj[1],action] += delta | |
# Update variables | |
tot_reward += reward | |
state_adj = state2_adj | |
# Decay epsilon | |
if epsilon > min_eps: | |
epsilon -= reduction | |
# Track rewards | |
reward_list.append(tot_reward) | |
if (i+1) % 100 == 0: | |
ave_reward = np.mean(reward_list) | |
ave_reward_list.append(ave_reward) | |
reward_list = [] | |
if (i+1) % 100 == 0: | |
print('Episode {} Average Reward: {}'.format(i+1, ave_reward)) | |
env.close() | |
return ave_reward_list | |
# Run Q-learning algorithm | |
rewards = QLearning(env, 0.2, 0.9, 0.8, 0, 5000) | |
# Plot Rewards | |
plt.plot(100*(np.arange(len(rewards)) + 1), rewards) | |
plt.xlabel('Episodes') | |
plt.ylabel('Average Reward') | |
plt.title('Average Reward vs Episodes') | |
plt.savefig('rewards.jpg') | |
plt.close() | |
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
in line 36:
state_adj = (state - env.observation_space.low)*np.array([10, 100])
# %%
import gymnasium as gym
import numpy as np
import imageio
env = gym.make("MountainCar-v0")
env.reset()
LEARNING_RATE = 0.1
DISCOUNT = 0.95
EPISODES = 1200
SHOW_EVERY = 200
DISCRETE_OS_SIZE = [20] * len(env.observation_space.high)
discrete_os_win_size = (env.observation_space.high - env.observation_space.low) / DISCRETE_OS_SIZE
q_table = np.random.uniform(low=-2, high=0, size=(DISCRETE_OS_SIZE + [env.action_space.n]))
def get_discrete_state(state):
discrete_state = (state - env.observation_space.low) / discrete_os_win_size
return tuple(discrete_state.astype(np.int32))
for episode in range(EPISODES):
discrete_state = get_discrete_state(env.reset()[0])
frames = []
done = False
while not done:
action = np.argmax(q_table[discrete_state])
new_state, reward, truncated, terminated, _ = env.step(action)
done = truncated or terminated
new_discrete_state = get_discrete_state(new_state)
if not done:
max_future_q = np.max(q_table[new_discrete_state])
current_q = q_table[discrete_state + (action, )]
new_q = (1 - LEARNING_RATE) * current_q + LEARNING_RATE * (reward + DISCOUNT * max_future_q)
q_table[discrete_state + (action, )] = new_q
elif new_state[0] >= env.goal_position:
print(f"Congratulation! We reached to the goal! Episode: {episode}")
q_table[discrete_state + (action, )] = 0
discrete_state = new_discrete_state
env.close()
# %%
env = gym.make("MountainCar-v0", render_mode='human')
env.reset()
DISCRETE_OS_SIZE = [20] * len(env.observation_space.high)
discrete_os_win_size = (env.observation_space.high - env.observation_space.low) / DISCRETE_OS_SIZE
discrete_state = get_discrete_state(env.reset()[0])
done = False
while not done:
discrete_state = get_discrete_state(env.state)
action = np.argmax(q_table[discrete_state])
new_state, _, done, _, _ = env.step(action)
env.close()
# %%
env.close()
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TypeError: unsupported operand type(s) for -: 'dict' and 'float'