def main():
env = gym.envs.make("MountainCar-v0")
# Feature Preprocessing: Normalize to zero mean and unit variance
# We use a few samples from the observation space to do this
observation_examples = np.array(
[env.observation_space.sample() for x in range(10000)])
scaler = sklearn.preprocessing.StandardScaler()
scaler.fit(observation_examples)
# Used to convert a state to a featurized represenation.
# We use RBF kernels with different variances to cover different parts of the space
featurizer = sklearn.pipeline.FeatureUnion([
("rbf1", RBFSampler(gamma=5.0, n_components=100)),
("rbf2", RBFSampler(gamma=2.0, n_components=100)),
("rbf3", RBFSampler(gamma=1.0, n_components=100)),
("rbf4", RBFSampler(gamma=0.5, n_components=100))
])
featurizer.fit(scaler.transform(observation_examples))
estimator = Estimator(env, scaler, featurizer)
# Note: For the Mountain Car we don't actually need an epsilon > 0.0
# because our initial estimate for all states is too "optimistic" which leads
# to the exploration of all states.
stats = q_learning(env, estimator, 100, epsilon=0.0)
plotting.plot_cost_to_go_mountain_car(env, estimator)
plotting.plot_episode_stats(stats, smoothing_window=25)
def run_qlearning(self,
max_number_of_episodes=100,
interactive=False,
display_frequency=1):
if interactive:
plt.ion()
plt.show()
else:
plt.close()
# repeat for each episode
for episode_number in range(max_number_of_episodes):
# initialize state
state = self.env.reset()
done = False # used to indicate terminal state
R = 0 # used to display accumulated rewards for an episode
t = 0 # used to display accumulated steps for an episode i.e episode length
# repeat for each step of episode, until state is terminal
while not done:
t += 1 # increase step counter - for display
# choose action from state using policy derived from Q
action = self.agent.act(state)
# take action, observe reward and next state
next_state, reward, done, _ = self.env.step(action)
# agent learn (Q-Learning update)
self.agent.learn(state, action, reward, next_state, done)
# state <- next state
state = next_state
R += reward # accumulate reward - for display
# if interactive display, show update for each step
if interactive:
self.update_display_step()
self.episode_length = np.append(
self.episode_length, t) # keep episode length - for display
self.episode_reward = np.append(
self.episode_reward, R) # keep episode reward - for display
# if interactive display, show update for the episode
if interactive:
self.update_display_episode()
# if not interactive display, show graph at the end
if not interactive:
self.fig.clf()
stats = plotting.EpisodeStats(
episode_lengths=self.episode_length,
episode_rewards=self.episode_reward,
episode_running_variance=np.zeros(max_number_of_episodes))
plotting.plot_episode_stats(stats, display_frequency)
def main():
print "PolyRL Q Learning"
#discretizing the action space
action_space = np.linspace(env.min_action, env.max_action, num=10)
w_param = np.random.normal(size=(400, action_space.shape[0] + 1))
print "Action Space", action_space
num_episodes = 200
smoothing_window = 100
stats_poly_q_learning = poly_rl_q_learning(env,
w_param,
num_episodes,
epsilon=0.1)
rewards_smoothed_stats_poly_q_learning = pd.Series(
stats_poly_q_learning.episode_rewards).rolling(
smoothing_window, min_periods=smoothing_window).mean()
cum_rwd = rewards_smoothed_stats_poly_q_learning
np.save(
'/Users/Riashat/Documents/PhD_Research/BASIC_ALGORITHMS/My_Implementations/Persistence_Length_Exploration/Results/'
+ 'Trial_PolyRL' + '.npy', cum_rwd)
plotting.plot_episode_stats(stats_poly_q_learning)
env.close()
def main():
print "Tree Backup (lambda)"
theta = np.zeros(shape=(400, env.action_space.n))
num_episodes = 1000
smoothing_window = 1
# stats_sarsa_tb_lambda, cumulative_errors = q_sigma_lambda_on_policy_static_sigma(env, theta, num_episodes)
# rewards_smoothed_stats_tb_lambda = pd.Series(stats_sarsa_tb_lambda.episode_rewards).rolling(smoothing_window, min_periods=smoothing_window).mean()
# cum_rwd = rewards_smoothed_stats_tb_lambda
# cum_err = cumulative_errors
# np.save('/Users/Riashat/Documents/PhD_Research/BASIC_ALGORITHMS/My_Implementations/Project_652/Code/Linear_Approximator/Eligibility_Traces/Accumulating_Traces/WindyGrid_Results/' + 'Q_sigma_lambda_OnPolicy_Static_Sigma_RBF_Cum_Rwd_2' + '.npy', cum_rwd)
# np.save('/Users/Riashat/Documents/PhD_Research/BASIC_ALGORITHMS/My_Implementations/Project_652/Code/Linear_Approximator/Eligibility_Traces/Accumulating_Traces/WindyGrid_Results/' + 'Q_sigma_lambda_OnPolicy_Static_Sigma_RBF_Cum_Err_2' + '.npy', cum_err)
# plotting.plot_episode_stats(stats_sarsa_tb_lambda)
# env.close()
stats_sarsa_tb_lambda, cumulative_errors = q_sigma_lambda_on_policy_dynamic_sigma(
env, theta, num_episodes)
rewards_smoothed_stats_tb_lambda = pd.Series(
stats_sarsa_tb_lambda.episode_rewards).rolling(
smoothing_window, min_periods=smoothing_window).mean()
cum_rwd = rewards_smoothed_stats_tb_lambda
cum_err = cumulative_errors
np.save(
'/Users/Riashat/Documents/PhD_Research/BASIC_ALGORITHMS/My_Implementations/Project_652/Code/Linear_Approximator/Eligibility_Traces/Accumulating_Traces/WindyGrid_Results/'
+ 'Q_sigma_lambda_OnPolicy_Dynamic_Sigma_RBF_Cum_Rwd_2' + '.npy',
cum_rwd)
np.save(
'/Users/Riashat/Documents/PhD_Research/BASIC_ALGORITHMS/My_Implementations/Project_652/Code/Linear_Approximator/Eligibility_Traces/Accumulating_Traces/WindyGrid_Results/'
+ 'Q_sigma_lambda_OnPolicy_Dynamic_Sigma_RBF_Cum_Err_2' + '.npy',
cum_err)
plotting.plot_episode_stats(stats_sarsa_tb_lambda)
env.close()
def test_td_control_method(env):
"""
plot_episode_stats([test_expected_sarsa_method(env),test_n_setps_expected_sarsa_method(env),test_off_policy_n_steps_sarsa(env),
test_n_steps_sarsa_method(env),test_qlearning_method(env),test_sarsa_lambda_method(env),test_q_lambda_method(env)])
"""
plot_episode_stats([test_qlearning_method(env),test_q_lambda_method(env),test_double_q_learning_method(env),test_dynaQ_method_trival(env),test_dynaQ_method_priority(env)])
def main():
env = gym.make('MountainCar-v0')
outdir = './experiment-results'
# env = wrappers.Monitor(env, directory=outdir, force=True)
# Keeps track of useful statistics
num_episodes = 300
stats = plotting.EpisodeStats(episode_lengths=np.zeros(num_episodes),
episode_rewards=np.zeros(num_episodes))
# Feature Preprocessing: Normalize to zero mean and unit variance
# We use a few samples from the observation space to do this
observation_examples = np.array(
[env.observation_space.sample() for x in range(10000)])
scaler = sklearn.preprocessing.StandardScaler()
scaler.fit(observation_examples)
# Used to converte a state to a featurizes represenation.
# We use RBF kernels with different variances to cover different parts of the space
featurizer = sklearn.pipeline.FeatureUnion([
("rbf1", RBFSampler(gamma=5.0, n_components=100)),
("rbf2", RBFSampler(gamma=2.0, n_components=100)),
("rbf3", RBFSampler(gamma=1.0, n_components=100)),
("rbf4", RBFSampler(gamma=0.5, n_components=100))
])
featurizer.fit(scaler.transform(observation_examples))
agent = Agent(env.action_space.n,
scaler,
featurizer,
env.observation_space.sample(),
epsilon=0,
gamma=1)
for i_episode in range(num_episodes):
print("\rEpisode {}/{} ({})".format(
i_episode + 1, num_episodes, stats.episode_rewards[i_episode - 1]),
end="")
sys.stdout.flush()
state = env.reset()
action = agent.set_initial_state(state)
for t in itertools.count():
next_state, reward, done, info = env.step(action)
action = agent.act(next_state, reward)
# book-keeping
stats.episode_rewards[i_episode] += reward
stats.episode_lengths[i_episode] = t
if done:
break
env.close()
# gym.upload(outdir, api_key='sk_9YxUhFDaT5XSahcLut47w')
plotting.plot_cost_to_go_mountain_car(env, agent.Q)
plotting.plot_episode_stats(stats, smoothing_window=25)
def main():
matplotlib.style.use('ggplot')
env = gym.envs.make("MountainCar-v0")
num_episodes = 100
estimator_q_learning = tile_coding_estimator.Estimator(env)
statistics_q_learning = plotting.EpisodeStats(
"q_learning",
episode_lengths=np.zeros(num_episodes),
episode_rewards=np.zeros(num_episodes))
# Note: For the Mountain Car we don't actually need an epsilon > 0.0
# because our initial estimate for all states is too "optimistic" which leads
# to the exploration of all states.
q_learning_tile_coding.q_learning(env,
estimator_q_learning,
num_episodes,
statistics_q_learning,
epsilon=0.0)
plotting.plot_cost_to_go_mountain_car(env, estimator_q_learning)
estimator_sarsa = tile_coding_estimator.Estimator(env)
statistics_sarsa = plotting.EpisodeStats(
"sarsa",
episode_lengths=np.zeros(num_episodes),
episode_rewards=np.zeros(num_episodes))
# Note: For the Mountain Car we don't actually need an epsilon > 0.0
# because our initial estimate for all states is too "optimistic" which leads
# to the exploration of all states.
sarsa_tile_coding.sarsa(env,
estimator_sarsa,
num_episodes,
statistics_sarsa,
epsilon=0.0)
plotting.plot_cost_to_go_mountain_car(env, estimator_sarsa)
estimator_expected_sarsa = tile_coding_estimator.Estimator(env)
statistics_expected_sarsa = plotting.EpisodeStats(
"expected_sarsa",
episode_lengths=np.zeros(num_episodes),
episode_rewards=np.zeros(num_episodes))
# Note: For the Mountain Car we don't actually need an epsilon > 0.0
# because our initial estimate for all states is too "optimistic" which leads
# to the exploration of all states.
expected_sarsa_tile_coding.expected_sarsa(env,
estimator_expected_sarsa,
num_episodes,
statistics_expected_sarsa,
epsilon=0.0)
plotting.plot_cost_to_go_mountain_car(env, estimator_expected_sarsa)
plotting.plot_episode_stats(
[statistics_q_learning, statistics_sarsa, statistics_expected_sarsa],
smoothing_window=25)
def main():
estimator = Estimator()
num_episodes = 5000
stats = q_learning(env, estimator, num_episodes, epsilon=0.1)
cum_rwd = save_cum_rwd(stats, smoothing_window=1)
plotting.plot_episode_stats(stats)
env.close()
def test_approximation_control_method(env):
episode_stats = [
test_approximation_control_sarsa(env),
test_approximation_control_expected_sarsa(env),
test_approximation_control_q_learning(env)
]
plotting.plot_episode_stats(episode_stats)
plotting.plot_3d_q_value(env, episode_stats)
def main():
Q, stats = sarsa(env, 50000)
# plotting.plot_episode_stats(stats)
# V = defaultdict(float)
# for state, actions in Q.items():
# action_value = np.max(actions)
# V[state] = action_value
# plotting.plot_value_function(V, title="Optimal Value Function")
plotting.plot_episode_stats(stats)
def main():
global num_of_load
global num_of_dump
global num_of_return
global state
global old_state
global old_time
global Mean_TD_Error
global Iterations
global nTrucks
BucketA_capacity = 1.5
BucketB_capacity = 1.0
Truck1_capacity = 6
Truck2_capacity = 3
Truck1_speed = 15.0
Truck2_speed = 20.0
Truck1_speedRatio = Truck1_speed / (Truck1_speed + Truck2_speed)
Truck2_speedRatio = Truck2_speed / (Truck1_speed + Truck2_speed)
#run session (initialise tf global vars)
sess.run(init)
num_episodes = 200
# Keeps track of useful statistics
stats = plotting.EpisodeStats(episode_lengths=np.zeros(num_episodes),
episode_rewards=np.zeros(num_episodes),
episode_loss=np.zeros(num_episodes))
for i_episode in range(num_episodes):
#reset global vars
num_of_load = 0
num_of_dump = 0
num_of_return = 0
state = np.zeros(12)
old_state = np.zeros((nTrucks, 12))
old_time = np.zeros(nTrucks)
Mean_TD_Error = 0
Iterations = 0
# Print out which episode we're on, useful for debugging.
if i_episode % 1 == 0:
print "\rEpisode: ", i_episode + 1, " / ", num_episodes
#run simulation
run_sim(nTrucks, BucketA_capacity, BucketB_capacity, Truck1_capacity,
Truck2_capacity, Truck1_speedRatio, Truck2_speedRatio)
stats.episode_lengths[i_episode] = Hrs[i_episode]
stats.episode_rewards[i_episode] = ProdRate[i_episode]
stats.episode_loss[i_episode] = abs(Mean_TD_Error)
plotting.plot_episode_stats(stats,
name='Linear_Qlearning',
smoothing_window=20)
def main():
theta = np.random.normal(size=(400,env.action_space.n))
num_episodes = 200
print "Running for Total Episodes", num_episodes
smoothing_window = 1
stats_q_sigma_off_policy = Q_Sigma_Off_Policy_2_Step(env, theta, num_episodes, epsilon=0.1)
rewards_smoothed_stats_q_sigma_off_policy = pd.Series(stats_q_sigma_off_policy.episode_rewards).rolling(smoothing_window, min_periods=smoothing_window).mean()
cum_rwd = rewards_smoothed_stats_q_sigma_off_policy
np.save('/Users/Riashat/Documents/PhD_Research/BASIC_ALGORITHMS/My_Implementations/Project_652/Code/Linear_Approximator/raw_results/' + 'Off_Policy_Q_Sigma_2_step' + '.npy', cum_rwd)
plotting.plot_episode_stats(stats_q_sigma_off_policy)
env.close()
def main():
print "SARSA(lambda)"
theta = np.zeros(shape=(total_states))
num_episodes = 5000
smoothing_window = 1
stats_sarsa_lambda = sarsa_lambda(env, theta, num_episodes)
rewards_smoothed_stats_sarsa_lambda = pd.Series(stats_sarsa_lambda.episode_rewards).rolling(smoothing_window, min_periods=smoothing_window).mean()
cum_rwd = rewards_smoothed_stats_sarsa_lambda
np.save('/Users/Riashat/Documents/PhD_Research/BASIC_ALGORITHMS/My_Implementations/Project_652/Code/Linear_Approximator/Eligibility_Traces/Accumulating_Traces/Cliff_Walking_Results/' + 'sarsa_lambda_rbf' + '.npy', cum_rwd)
plotting.plot_episode_stats(stats_sarsa_lambda)
env.close()
def main():
tf.reset_default_graph()
global_step = tf.Variable(0, name="global_step", trainable=False)
policy_estimator = PolicyEstimator()
value_estimator = ValueEstimator()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Note, due to randomness in the policy the number of episodes you need to learn a good
# policy may vary. ~300 seemed to work well for me.
stats = actor_critic(env, policy_estimator, value_estimator, 300)
plotting.plot_episode_stats(stats, smoothing_window=25)
def main():
print "Q Learning"
theta = np.random.normal(size=(400, env.action_space.n))
num_episodes = 2000
smoothing_window = 200
stats_q_learning = q_learning(env, theta, num_episodes, epsilon=0.1)
rewards_smoothed_stats_q_learning = pd.Series(
stats_q_learning.episode_rewards).rolling(
smoothing_window, min_periods=smoothing_window).mean()
cum_rwd = rewards_smoothed_stats_q_learning
np.save(
'/Users/Riashat/Documents/PhD_Research/BASIC_ALGORITHMS/My_Implementations/Persistence_Length_Exploration/Results/'
+ 'Trial_Q_Learning' + '.npy', cum_rwd)
plotting.plot_episode_stats(stats_q_learning)
env.close()
def q_learning_cliff_walking(Q,
env,
eps=200,
epsilon=0.05,
alpha=0.5,
discount_factor=1.0,
timesteps=800):
nS = env.nS
nA = env.nA
episode_reward = []
episode_length = []
init_epsilon = epsilon
for ep in range(eps):
epsilon -= (init_epsilon * 0.0005)
epsilon = max(epsilon, 0)
policy = fn_policy(Q, env, epsilon=epsilon)
current_state = env.reset()
current_action = np.random.choice(np.arange(nA),
p=policy[current_state])
total_reward = 0
for ts in range(timesteps):
next_state, reward, done, prob = env.step(current_action)
total_reward += reward
next_action = np.random.choice(np.arange(nA), p=policy[next_state])
next_greedy_action = np.argmax(policy[next_state])
Q[current_state][
current_action] = Q[current_state][current_action] + alpha * (
reward +
(discount_factor * Q[next_state][next_greedy_action]) -
Q[current_state][current_action])
next_action_array = np.ones(nA) * epsilon / nA
next_action_array[np.argmax(Q[current_state])] += (1 - epsilon)
policy[current_state] = next_action_array
if done:
print(
"Episode {} ended after {} timesteps with total reward of {}"
.format(ep, ts, total_reward))
episode_length.append(ts)
episode_reward.append(total_reward)
break
current_state = next_state
current_action = next_action
stats = plotting.EpisodeStats(episode_lengths=episode_length,
episode_rewards=episode_reward)
plotting.plot_episode_stats(stats)
def main():
tf.reset_default_graph()
global_step = tf.Variable(0, name="global_step", trainable=False)
policy_estimator = PolicyEstimator()
value_estimator = ValueEstimator(env)
num_episodes = 1000
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
stats = actor_critic(env, policy_estimator, value_estimator,
num_episodes)
plotting.plot_episode_stats(stats, smoothing_window=25)
env.close()
def main():
print "True Online Tree Backup (lambda)"
# theta = np.random.normal(size=(400))
theta = np.zeros(shape=(400, env.action_space.n))
num_episodes = 1000
smoothing_window = 1
stats_sarsa_tb_lambda, cumulative_errors = true_online_tree_backup_lambda(env, theta, num_episodes)
rewards_smoothed_stats_tb_lambda = pd.Series(stats_sarsa_tb_lambda.episode_rewards).rolling(smoothing_window, min_periods=smoothing_window).mean()
cum_rwd = rewards_smoothed_stats_tb_lambda
cum_err = cumulative_errors
np.save('/Users/Riashat/Documents/PhD_Research/BASIC_ALGORITHMS/My_Implementations/Project_652/Code/Linear_Approximator/Eligibility_Traces/Accumulating_Traces/WindyGrid_Results/' + 'True_Online_Tree_Backup_RBF_Cum_Rwd_2' + '.npy', cum_rwd)
np.save('/Users/Riashat/Documents/PhD_Research/BASIC_ALGORITHMS/My_Implementations/Project_652/Code/Linear_Approximator/Eligibility_Traces/Accumulating_Traces/WindyGrid_Results/' + 'True_Online_Tree_Backup_RBF_Cum_Err_2' + '.npy', cum_err)
plotting.plot_episode_stats(stats_sarsa_tb_lambda)
env.close()
def main():
env = CliffWalkingEnv()
num_episodes = 500
tf.reset_default_graph()
tf.Variable(0, name="global_step", trainable=False)
policyEstimatorReinforce = policy_estimator.PolicyEstimator(
env, scope="Policy_Estimator_Reinforce")
valueEstimatorReinforce = value_estimator.ValueEstimator(
env, scope="Value_Estimator_Reinforce")
statistics_reinforce = plotting.EpisodeStats(
"Reinforce",
episode_lengths=np.zeros(num_episodes),
episode_rewards=np.zeros(num_episodes))
policyEstimatorAC = policy_estimator.PolicyEstimator(
env, scope="Policy_Estimator_AC")
valueEstimatorAC = value_estimator.ValueEstimator(
env, scope="Value_Estimator_AC")
statistics_ac = plotting.EpisodeStats(
"AC",
episode_lengths=np.zeros(num_episodes),
episode_rewards=np.zeros(num_episodes))
with tf.Session() as session:
session.run(tf.global_variables_initializer())
# Note, due to randomness in the policy the number of episodes you need to learn a good
# policy may vary. ~2000-5000 seemed to work well for me.
reinforce.reinforce(env,
statistics_reinforce,
policyEstimatorReinforce,
valueEstimatorReinforce,
num_episodes,
discount_factor=1.0)
actor_critic.actor_critic(env, statistics_ac, policyEstimatorAC,
valueEstimatorAC, num_episodes)
plotting.plot_episode_stats([statistics_reinforce, statistics_ac],
smoothing_window=25)
def main():
parser = argparse.ArgumentParser(description='Run Reinforcment Learning at an Office in Tsinghua University')
parser.add_argument('--env', default='band_control-v0', help='Environment name')
parser.add_argument('-o', '--output', default='office-QN-Rh', help='Directory to save data to')
parser.add_argument('--num', default=500, help='Number of Episodes')
parser.add_argument('--gamma', default=0.95, help='Discount Factor')
parser.add_argument('--alpha', default=0.5, help='Constant step-size parameter')
parser.add_argument('--epsilon', default=0.05, help='Epsilon greedy policy')
parser.add_argument('--epsilon_min', default=0.05, help='Smallest Epsilon that can get')
parser.add_argument('--epsilon_decay', default=0.9, help='Epsilon decay after the number of episodes')
parser.add_argument('--batch_size', default=32, help='Sampling batch size')
parser.add_argument('--lr', default=0.001, help='Learning rate')
args = parser.parse_args()
output = get_output_folder(args.output, args.env)
#create environment
print(args.env)
env = gym.make(args.env)
################# tabular Q learning ##########
#### change the environment to _process_state_table before use it
# Q, stats = QL.q_learning(env, int(args.num), float(args.gamma), float(args.alpha), float(args.epsilon),
# float(args.epsilon_min), float(args.epsilon_decay), output)
# plotting.plot_episode_stats(stats, smoothing_window=1)
# print(Q)
############### Q learning with Neural network approximation and fixed target ################
#### change the environment to _process_state_DDQN before use it
state_size = env.nS
action_size = env.nA
agent = QN.QNAgent(state_size, action_size, float(args.gamma), float(args.lr))
stats = QN.q_learning(env, agent, int(args.num), int(args.batch_size),
float(args.epsilon), float(args.epsilon_min), float(args.epsilon_decay), output)
plotting.plot_episode_stats(stats, smoothing_window=1)
def main():
estimator = Estimator()
number_of_episodes = 1000
print('Two Step Sarsa')
stats_sarsa_2_step = sarsa_2_step_TD(env,
estimator,
number_of_episodes,
discount_factor=1.0,
epsilon=0.015,
epsilon_decay=1.0)
plotting.plot_episode_stats(stats_sarsa_2_step, smoothing_window=25)
print('Two Step Q Learning')
stats_Q = Q_learning_2_step_TD(env,
estimator,
number_of_episodes,
discount_factor=1,
epsilon=0.015,
epsilon_decay=1.0)
print('Two Step Tree Backup')
stats_tree = two_step_tree_backup(env,
estimator,
number_of_episodes,
discount_factor=1.0,
epsilon=0.1)
print('SARSA')
stats_sarsa = sarsa(env,
estimator,
number_of_episodes,
discount_factor=1.0,
epsilon=0.015,
epsilon_decay=1.0)
print('Expected SARSA')
stats_expected_sarsa = expected_sarsa(env,
estimator,
number_of_episodes,
discount_factor=1.0,
epsilon=0.015,
epsilon_decay=1.0)
plot_episode_stats(stats_sarsa_2_step, stats_Q, stats_tree, stats_sarsa,
stats_expected_sarsa)
def sarsa_WindyGridWorld(Q,
env,
eps=200,
discount_factor=1.0,
alpha=0.5,
epsilon=0.05):
nS = env.nS
nA = env.nA
episode_lengths = []
episode_rewards = []
for ep in range(eps):
timesteps = 700
current_state = env.reset()
policy = fn_policy(env, Q, epsilon=epsilon)
action_arr = policy[current_state]
action = np.random.choice(np.arange(nA), p=action_arr)
total_reward = 0
for ts in range(timesteps):
next_state, reward, done, prob = env.step(action)
total_reward += reward
next_action_arr = policy[next_state]
next_action = np.random.choice(np.arange(nA), p=next_action_arr)
Q[current_state][action] = Q[current_state][action] + alpha * (
reward + (discount_factor * Q[next_state][next_action]) -
Q[current_state][action])
act_arr = np.ones(nA) * epsilon / nA
act_arr[np.argmax(Q[current_state])] += (1 - epsilon)
policy[current_state] = act_arr
if done:
print("Episode {} ended after {} timesteps".format(ep, ts))
episode_lengths.append(ts)
episode_rewards.append(total_reward)
break
current_state = next_state
action = next_action
stats = plotting.EpisodeStats(episode_lengths=np.array(episode_lengths),
episode_rewards=np.array(episode_rewards))
plotting.plot_episode_stats(stats)
def main():
Q, stats = q_learning(env, 300)
plotting.plot_episode_stats(stats)
import sys
if "../" not in sys.path:
sys.path.append("../")
from lib.envs.cliff_walking import CliffWalkingEnv
from lib import plotting
from agents import QLearningAgent
import numpy as np
env_shape = (4, 12)
start_position = (3, 0)
end_positions = [(3, 11)]
cliff = tuple((3, i + 1) for i in range(10))
env = CliffWalkingEnv(env_shape, start_position, end_positions, cliff)
n_actions = env.action_space.n
agent = QLearningAgent(alpha=0.5,
epsilon=0.1,
discount=0.99,
n_actions=n_actions)
agent.train(env,
n_episodes=1000,
t_max=10**3,
verbose=True,
verbose_per_episode=500)
plotting.draw_policy(env, agent)
plotting.plot_episode_stats(agent)
if update_time >= 0:
action_state_update_time = env_list[update_time][1]
evaluated_state_index = update_time + self.n - 1
if evaluated_state_index < len(states):
state_update_time = states[evaluated_state_index]
action_state_update_time.update(
0,
state_update_time.get_actions(),
time_step=update_time)
else:
action_state_update_time.update(0,
None,
time_step=update_time)
if update_time == T - 1:
a_ss = [a_s for _, a_s in env_list]
for a_s in a_ss:
a_s.clear_reward_calculator()
break
return stats
if __name__ == '__main__':
q_learning = NStepSarsa(CliffWalkingEnv(), 1)
stats = q_learning.run(200, get_learning_rate=lambda x1, x2: 1)
plotting.plot_episode_stats(stats)
q_learning.show_one_episode()
# q_learning = NStepSarsa(WindyGridworldEnv(), 8)
# stats = q_learning.run(50000)
# plotting.plot_episode_stats(stats)
# q_learning.show_one_episode()
# next_action = np.random.choice(np.arange(len(next_action_probs)), p=next_action_probs)
# td_target = reward + discount_factor * q_values_next[next_action]
# Update the function approximator using our target
estimator.update(state, action, td_target)
#plt.figure()
plt.clf()
plt.imshow(env.render(mode='rgb_array'))
print("\rStep {} @ Episode {}/{} ({})".format(
t, i_episode + 1, num_episodes, last_reward)),
sys.stdout.flush()
if done:
break
state = next_state
return stats
estimator = Estimator()
# Note: For the Mountain Car we don't actually need an epsilon > 0.0
# because our initial estimate for all states is too "optimistic" which leads
# to the exploration of all states.
stats = q_learning(env, estimator, 100, epsilon=0.0)
plotting.plot_cost_to_go_mountain_car(env, estimator)
plotting.plot_episode_stats(stats, smoothing_window=25)
target_estimator=target_estimator,
state_processor=state_processor,
experiment_dir=experiment_dir,
num_episodes=3000,
replay_memory_size=200000,
replay_memory_init_size=20000,
update_target_estimator_every=10000,
epsilon_start=1,
epsilon_end=0.1,
epsilon_decay_steps=500000,
discount_factor=0.99,
batch_size=32,
record_video_every=50):
episode_reward_array.append(stats.episode_rewards[-1])
if num_episode % 50 == 0:
fig1, fig2, fig3, fig4 = plotting.plot_episode_stats(stats, smoothing_window=10, noshow=True)
fig1.savefig(experiment_dir + '/episode_length.jpg')
fig2.savefig(experiment_dir + '/reward.jpg')
fig3.savefig(experiment_dir + '/episode_per_t.jpg')
fig4.savefig(experiment_dir + '/episode_reward.jpg')
np.savetxt(experiment_dir + '/episode_reward.txt', episode_reward_array, newline=" ")
num_episode += 1
avg_50_reward.append(np.average(stats.episode_rewards[max(0, num_episode - 50):]))
print("\nEpisode Reward: {} , Last 50 average: {}".format(stats.episode_rewards[-1], np.average(
stats.episode_rewards[max(0, num_episode - 50):])))
# In[ ]:
def plot(self, stats):
plotting.plot_episode_stats(stats)
from collections import defaultdict
import matplotlib
import numpy as np
sys.path.append('/home/ornot/GymRL')
from algorithm import mc_online_policy_control, q_learning, sarsa, expected_sarsa
from env import windy_gridworld
from lib import plotting
matplotlib.style.use('ggplot')
env = windy_gridworld.WindyGridworldEnv()
num_episodes = 200
# TD online
statistics_sara = plotting.EpisodeStats("sara",
episode_lengths=np.zeros(num_episodes),
episode_rewards=np.zeros(num_episodes))
Q_sara = sarsa.sarsa(env, num_episodes, statistics_sara)
# TD_offline
statistics_q_learning = plotting.EpisodeStats(
"q_learning",
episode_lengths=np.zeros(num_episodes),
episode_rewards=np.zeros(num_episodes))
Q_learning = q_learning.q_learning(env, num_episodes, statistics_q_learning)
plotting.plot_episode_stats([statistics_sara, statistics_q_learning])
def main():
Q, stats = sarsa(env, 500)
plotting.plot_episode_stats(stats)
# update current state
state = new_state
if terminated:
break
return stats
# In[122]:
estimator = Estimator()
# In[123]:
# Note: For the Mountain Car we don't actually need an epsilon > 0.0
# because our initial estimate for all states is too "optimistic" which leads
# to the exploration of all states.
stats = q_learning(env, estimator, 100, epsilon=0.0)
# In[124]:
plotting.plot_cost_to_go_mountain_car(env, estimator)
plotting.plot_episode_stats(stats, smoothing_window=25)