def main():
new_map = ["SFFF", "FHFH", "FFFH", "HFFG"]
env = FrozenLakeEnv(desc=new_map, is_slippery=IS_SLIPPERY)
env = env.unwrapped
succeed_episode = 0
for i_episode in range(1000000):
if use_random_map and i_episode % 10 == 0:
env.close()
new_map = random_map(HOLE_NUM)
env = FrozenLakeEnv(desc=new_map, is_slippery=IS_SLIPPERY)
env = env.unwrapped
pos = env.reset()
state = encode_state(new_map, pos)
ep_r = 0
while True:
a = select_action(state)
pos_next, r, done, info = env.step(a)
ep_r += r
#state_next = encode_state(new_map, pos_next)
if args.render:
env.render()
model.rewards.append(r)
if done:
break
finish_episode()
episode_durations.append(ep_r)
if ep_r > 0:
# EPSILON = 1 - 1. / ((i_episode / 500) + 10)
succeed_episode += 1
if i_episode % 1000 == 1:
print('EP: {:d} succeed rate {:4f}'.format(i_episode,
succeed_episode / 1000))
succeed_episode = 0
if i_episode % 5000 == 1:
plot_durations()
def test_expected(self):
env = FrozenLakeEnv(is_slippery=False)
policy = UserInputPolicy(env)
s = env.reset()
env.render()
for i in [RIGHT, RIGHT, DOWN, DOWN, DOWN, RIGHT]:
with MockInputFunction(return_value=i):
a = policy(s)
s, r, done, info = env.step(a)
env.render()
if done:
break
else:
delta = (reward + gamma * Q_table[new_state, new_action] -
Q_table[state, action])
Q_table[state, action] += learning_rate * delta
reward_list = []
for k in range(N_trial + N_trial_test):
acc_reward = 0 # Init the accumulated reward
observation = env.reset() # Init the state
action = policy(Q_table, observation, epsilon) # Init the first action
for t in range(trial_duration):
if render: env.render()
new_observation, reward, done, info = env.step(
action) # Take the action
new_action = policy(Q_table, new_observation, epsilon)
update_Q_table(Q_table=Q_table,
state=observation,
action=action,
reward=reward,
new_state=new_observation,
new_action=new_action,
is_done=done)
observation = new_observation # Pass the new state to the next step
action = new_action # Pass the new action to the next step
acc_reward += reward # Accumulate the reward
import gym
import random
import numpy as np
from gym.envs.toy_text.frozen_lake import FrozenLakeEnv
char_list = list('SFFFFFFFFFFFFFFG')
for i in range(2):
char_list[random.randint(1, 14)] = 'H'
my_map = [''.join(char_list[i:i + 4]) for i in [0, 4, 8, 12]]
env = FrozenLakeEnv(desc=np.asarray(my_map, dtype='c'), is_slippery=False)
env = env.unwrapped
for i in range(10):
b = env.render()
a = env.step(1)
print(a)
# print(episode, qtable)
# print(total_rewards)
print("Score over time: " + str(sum(rewards) / total_episodes))
print(qtable)
env.reset()
for episode in range(1):
state = env.reset()
print("state",state)
step = 0
done = False
print("****************************************************")
print("EPISODE ", episode)
for step in range(max_steps):
env.render()
# Take the action (index) that have the maximum expected future reward given that state
action = np.argmax(qtable[state, :])
new_state, reward, done, info = env.step(action)
if done:
print("done..........")
# env.render()
break
state = new_state
env.close()