num_bins = [3, 20, 3, 6, 6, 6, 3, 3]
num_pos_actions = len(actions)
q_learning = QLearning(env=env,
num_bins=num_bins,
num_pos_actions=num_pos_actions,
env_ranges=env_ranges,
discount=0,
episodes=0,
epsilon=None,
lr=None,
USE=True)
env = gym.make('LunarLander-v2')
q_learning.q_table = np.load('./data_lunarlander/0_9000.npy')
for _ in range(10):
obs = q_learning.reset_state() # Reset the environment and get the initial
done = False
while not done:
action = q_learning.action_to_maximise_q(obs)
obs, reward, done = q_learning.perform_sim_step(action)
print(obs, reward, done)
q_learning.env.render()
env.close()
epsilon = [0.5, 1.0,
episodes // 2] # Epsilon start, start decay index, stop decay index
lr = [0.5, 1.0, episodes // 2
] # Learning rate start, start decay index, stop decay index
q_learning = QLearning(env, num_bins, num_pos_actions, env_ranges, discount,
episodes, epsilon, lr)
print('q-table shape', q_learning.q_table.shape)
obs = q_learning.reset_state() # Reset the environment and get the initial
obs = [obs[i] for i in obs_to_use]
print('\nInitial observation:', obs)
action_to_maximise_q = q_learning.action_to_maximise_q(
obs) # Find optimal action
action = q_learning.decide_on_action(
action_to_maximise_q) # Decide whether to use optimal or random action
observation, reward_current, done = q_learning.perform_sim_step(
action) # env.step(action) # Perform the first action
NUM_TO_SHOW = 5
rewards = []
while q_learning.episode < q_learning.episodes:
reward_sum = 0
if not q_learning.episode % (episodes // NUM_TO_SHOW):
render = True
print('episode, learning_rate, epsilon', q_learning.episode,