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
'''
if is_done:
delta = (reward - Q_table[state, action])
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)
max_steps = 99 # Max steps per episode
gamma = 0.95 # Discounting rate
# Exploration parameters
epsilon = 1.0 # Exploration rate
max_epsilon = 1.0 # Exploration probability at start
min_epsilon = 0.2 # Minimum exploration probability
decay_rate = 0.01 # Exponential decay rate for exploration prob
# List of rewards
rewards = []
# 2 For life or until learning is stopped
for episode in range(total_episodes):
# Reset the environment
state = env.reset()
step = 0
done = False
total_rewards = 0
for step in range(max_steps):
# 3. Choose an action a in the current world state (s)
## First we randomize a number
exp_exp_tradeoff = random.uniform(0, 1)
# print(exp_exp_tradeoff,epsilon)
## If this number > greater than epsilon --> exploitation (taking the biggest Q value for this state)
if exp_exp_tradeoff > epsilon:
action = np.argmax(qtable[state, :])
# print("action",action)
NUM_EPISODES = 500
# How often we print results
PRINT_EVERY_EPS = 100
environment = FrozenLakeEnv(is_slippery=False)
num_states = environment.observation_space.n
num_actions = environment.action_space.n
agent = QAgent(num_states, num_actions)
sum_reward = 0
for episode in range(NUM_EPISODES):
done = False
last_state = environment.reset()
last_reward = None
# Number of steps taken. A bit of a safeguard...
num_steps = 0
while not done:
# Epsilon-greedy policy
action = agent.get_action(last_state, environment)
state, reward, done, info = environment.step(action)
# A crude timeout: If we play too long without
# completing the level, kill the game
num_steps += 1
if num_steps > 1000:
print(
"Episode timeout! Could not finish in 1000 steps. Check your actions!"
epsilon = 0.5
#print(Q.shape)
def epsilon_policy(state, Q, epsilon):
a = np.argmax(Q[state, :])
if np.random.rand() < epsilon:
a = np.random.randint(num_actions)
#a = env.action_space.sample()
return a
averageepisodelength = []
for i in range(num_episodes):
episodelength = 0
state = env.reset()
totalreward = 0
rand = np.random.randn(1, env.action_space.n)
#action = random.randint(0,num_actions-1)
done = False
action = epsilon_policy(state, Q, epsilon)
#print(state,action)
while not done:
newstate, reward, done, q = env.step(action)
#print(newstate, reward, done , q)
newaction = epsilon_policy(newstate, Q, epsilon)
#newaction = np.argmax(Q[newstate, :] + np.random.randn(1, env.action_space.n) * (1. / (i + 1)) )
#print("A:",newaction)
Q[state, action] = Q[state, action] + alpha * (
reward + gamma * Q[newstate, newaction] - Q[state, action])
# define function approximators
func = LinearFunc(env, lr=0.01)
pi = km.SoftmaxPolicy(func, update_strategy='vanilla')
cache = km.caching.MonteCarloCache(env, gamma=0.99)
# static parameters
num_episodes = 250
num_steps = 30
# train
for ep in range(num_episodes):
s = env.reset()
cache.reset()
for t in range(num_steps):
a = pi(s)
s_next, r, done, info = env.step(a)
# small incentive to keep moving
if np.array_equal(s_next, s):
r = -0.1
cache.add(s, a, r, done)
if done:
while cache:
S, A, G = cache.pop()
