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(ft, D, [], [])
# init = tf.global_variables_initializer()
session = tf.InteractiveSession()
# session.run(init)
pmodel.set_session(session)
pmodel.init_vars()
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)
totalrewards, pmodel = random_search(env, pmodel, gamma)
print("max reward:", np.max(totalrewards))
# play 100 episodes and check the average
avg_totalrewards = play_multiple_episodes(env,
100,
pmodel,
gamma,
print_iters=True)
print("avg reward over 100 episodes with best models:", avg_totalrewards)
plt.plot(totalrewards)
plt.title("Rewards")
plt.show()
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)
def main():
env = gym.make('MountainCarContinuous-v0')
ft = FeatureTransformer(env)
D = ft.dimensions
pmodel = PolicyModel(ft, D, [], [])
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)
totalrewards, pmodel = random_search(env, pmodel, gamma)
print("max reward", np.max(totalrewards))
# play 100 episodes and check the average
avg_totalrewards = play_multiple_episodes(env,
100,
pmodel,
gamma,
print_iters=True)
print("avg reward over 100 episoes with best models:", avg_totalrewards)
plt.plot(totalrewards)
plt.title("Rewards")
plt.show()
actions.pop(0)
else:
while len(rewards) > 0:
guess_rewards = rewards + [-1] * (n - len(rewards))
G = multiplier.dot(guess_rewards)
model.update(states[0], actions[0], G)
rewards.pop(0)
states.pop(0)
actions.pop(0)
return totalreward
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
observation # In[71]: actions = np.atleast_1d(env.observation_space.sample()) # In[72]: observation, reward, done, info = env.step(actions) # In[73]: observation # In[74]: ft.transform(observations=observation) # In[33]: ft = FeatureTransformer(env, n_components=100) # In[37]: ft.transform(np.atleast_2d(env.reset())) # In[38]: np.atleast_2d(env.reset())
# an object where the actual action is stored in object[0]
observation, reward, done, info = env.step([action])
totalreward += reward
# Update models
V_next = vmodel.predict(observation)
G = reward + gamma * V_next
advantage = G - vmodel.predict(prev_observation)
pmodel.partial_fit(prev_observation, action, advantage)
vmodel.partial_fit(prev_observation, G)
iters += 1
return totalreward, iters
# In[7]:
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
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)
