def main():
env = gym.make('MountainCarContinuous-v0')
ft = FeatureTransformer(env, n_components=100)
D = ft.dimensions
pmodel = PolicyModel(D, ft)
vmodel = ValueModel(D, ft)
gamma = 0.99
if 'monitor' in sys.argv:
filename = os.path.basename(__file__).split('.')[0]
monitor_dir = './' + filename + '_' + str(datetime.now())
env = wrappers.Monitor(env, monitor_dir)
N = 50
totalrewards = np.empty(N)
costs = np.empty(N)
for n in range(N):
totalreward = play_one_td(env, pmodel, vmodel, gamma)
totalrewards[n] = totalreward
if n % 1 == 0:
print(
"episode:", n, "total reward: %.1f" % totalreward,
"avg reward (last 100): %.1f" %
totalrewards[max(0, n - 100):(n + 1)].mean())
print("avg reward for last 100 episodes:", totalrewards[-100:].mean())
plt.plot(totalrewards)
plt.title("Rewards")
plt.show()
plot_running_avg(totalrewards)
plot_cost_to_go(env, vmodel)
def main():
env = gym.make('MountainCarContinuous-v0')
ft = FeatureTransformer(env, n_components=100)
D = ft.dimensions
pmodel = PolicyModel(D, ft, [])
vmodel = ValueModel(D, ft, [])
init = tf.global_variables_initializer()
session = tf.InteractiveSession()
session.run(init)
pmodel.set_session(session)
vmodel.set_session(session)
gamma = 0.95
if 'monitor' in sys.argv:
filename = os.path.basename(__file__).split('.')[0]
monitor_dir = './' + filename + '_' + str(datetime.now())
env = wrappers.Monitor(env, monitor_dir)
N = 50
totalrewards = np.empty(N)
costs = np.empty(N)
for n in range(N):
totalreward, num_steps = play_one_td(env, pmodel, vmodel, gamma)
totalrewards[n] = totalreward
if n % 1 == 0:
print("episode:", n, "total reward: %.1f" % totalreward, "num steps: %d" % num_steps, "avg reward (last 100): %.1f" % totalrewards[max(0, n-100):(n+1)].mean())
print("avg reward for last 100 episodes:", totalrewards[-100:].mean())
plt.plot(totalrewards)
plt.title("Rewards")
plt.show()
plot_running_avg(totalrewards)
plot_cost_to_go(env, vmodel)
def main():
env = gym.make('MountainCarContinuous-v0')
ft = FeatureTransformer(env, n_components=100)
D = ft.dimensions
pmodel = PolicyModel(D, ft, [])
vmodel = ValueModel(D, ft, [])
init = tf.compat.v1.global_variables_initializer()
session = tf.compat.v1.InteractiveSession()
session.run(init)
pmodel.set_session(session)
vmodel.set_session(session)
gamma = 0.95
if 'monitor' in sys.argv:
filename = os.path.basename(__file__).split('.')[0]
monitor_dir = './' + filename + '_' + str(datetime.now())
env = wrappers.Monitor(env, monitor_dir)
N = 50
totalrewards = np.empty(N)
costs = np.empty(N)
for n in range(N):
totalreward, num_steps = play_one_td(env, pmodel, vmodel, gamma)
totalrewards[n] = totalreward
if n % 1 == 0:
print("episode:", n, "total reward: %.1f" % totalreward, "num steps: %d" % num_steps, "avg reward (last 100): %.1f" % totalrewards[max(0, n-100):(n+1)].mean())
print("avg reward for last 100 episodes:", totalrewards[-100:].mean())
plt.plot(totalrewards)
plt.title("Rewards")
plt.show()
plot_running_avg(totalrewards)
plot_cost_to_go(env, vmodel)
if __name__ == '__main__':
env = gym.make('MountainCar-v0')
ft = FeatureTransformer(env)
model = Model(env, ft, "constant")
gamma = 0.99
if 'monitor' in sys.argv:
filename = os.path.basename(__file__).split('.')[0]
monitor_dir = './' + filename + '_' + str(datetime.now())
env = wrappers.Monitor(env, monitor_dir)
N = 300
totalrewards = np.empty(N)
costs = np.empty(N)
for n in range(N):
eps = 0.1 * (0.97**n)
totalreward = play_one(model, eps, gamma)
totalrewards[n] = totalreward
print("episode:", n, "total reward:", totalreward)
print("avg reward for last 100 episodes:", totalrewards[-100:].mean())
print("total steps:", -totalrewards.sum())
plt.plot(totalrewards)
plt.title("Rewards")
plt.show()
plot_running_avg(totalrewards)
plot_cost_to_go(env, model)
lambda_ = 0.7
if 'monitor' in sys.argv:
filename = os.path.basename(__file__).split('.')[0]
monitor_dir = './' + filename + '_' + str(datetime.now())
env = wrappers.Monitor(env, monitor_dir)
N = 300
totalrewards = np.empty(N)
costs = np.empty(N)
for n in range(N):
# eps = 1.0/(0.1*n+1)
eps = 0.1*(0.97**n)
# eps = 0.5/np.sqrt(n+1)
totalreward = play_one(model, env, eps, gamma, lambda_)
totalrewards[n] = totalreward
print("episode:", n, "total reward:", totalreward)
print("avg reward for last 100 episodes:", totalrewards[-100:].mean())
print("total steps:", -totalrewards.sum())
plt.plot(totalrewards)
plt.title("Rewards")
plt.show()
plot_running_avg(totalrewards)
# plot the optimal state-value function
plot_cost_to_go(env, model)
