def question_1():
# Specify hyper-parameters
agent = Agent()
environment = Environment()
rlglue = RLGlue(environment, agent)
num_episodes = 200
num_runs = 50
max_eps_steps = 100000
steps = np.zeros([num_runs, num_episodes])
for r in range(num_runs):
print("run number : ", r)
rlglue.rl_init()
for e in range(num_episodes):
#print("Episode number: "+str(e))
rlglue.rl_episode(max_eps_steps)
steps[r, e] = rlglue.num_ep_steps()
#print("Number of steps: "+str(steps))
# print(steps[r, e])
np.save('steps', steps)
plotGraph()
del agent, environment, rlglue
agent = Agent()
environment = Environment()
rlglue = RLGlue(environment, agent)
num_episodes = 1000
num_runs = 1
max_eps_steps = 100000
steps = np.zeros([num_runs, num_episodes])
for r in range(num_runs):
print("run number : ", r)
rlglue.rl_init()
for e in range(num_episodes):
print("Episode number: "+str(e))
rlglue.rl_episode(max_eps_steps)
steps[r, e] = rlglue.num_ep_steps()
#print("Number of steps: "+str(steps))
# print(steps[r, e])
#np.save('steps', steps)
#plotGraph()
rlglue.rl_agent_message("plot3DGraph")
def question_3():
# Specify hyper-parameters
agent = Agent()
environment = Environment()
rlglue = RLGlue(environment, agent)
num_episodes = 1000
num_runs = 1
max_eps_steps = 1000000
for _ in range(num_runs):
rlglue.rl_init()
i = 0
for i in range(num_episodes):
rlglue.rl_episode(max_eps_steps)
print(i)
fout = open('value', 'w')
steps = 50
w, iht = rlglue.rl_agent_message("ValueFunction")
Q = np.zeros([steps, steps])
for i in range(steps):
for j in range(steps):
values = []
for a in range(3):
value = 0
for index in tiles(iht, 8, [
8 * (-1.2 + (i * 1.7 / steps)) / 1.7, 8 *
(-0.07 + (j * 0.14 / steps)) / 0.14
], [a]):
value -= w[index]
values.append(value)
height = max(values)
fout.write(repr(height) + ' ')
Q[j][i] = height
fout.write('\n')
fout.close()
np.save("value", Q)
def question_3():
agent = Agent()
environment = Environment()
rlglue = RLGlue(environment, agent)
max_eps_steps = 100000
num_episodes = 1000
num_runs = 1
numActions=3
rlglue.rl_init()
for e in range(num_episodes):
rlglue.rl_episode(max_eps_steps)
weights = rlglue.rl_agent_message("3D plot of the cast-to-go")
fout = open('value','w')
steps = 50
z = np.zeros((50,50))
for i in range(steps):
for j in range(steps):
values = []
for a in range(numActions):
tile = [8*(-1.2+(i*1.7/steps))/1.7,8*(-0.07+(j*0.14/steps))/0.14]
inds = agent.get_index(tile,a)
values.append(np.sum([weights[i] for i in inds]))
height = max(values)
z[j][i]=-height
fout.write(repr(-height)+' ')
fout.write('\n')
fout.close()
fig = plt.figure()
ax = fig.add_subplot(111,projection ='3d')
x = np.arange(-1.2,0.5,1.7/50)
y = np.arange(-0.07,0.07,0.14/50)
x,y = np.meshgrid(x,y)
ax.set_xticks([-1.2, 0.5])
ax.set_yticks([0.07, -0.07])
ax.set_ylabel('Velocity')
ax.set_xlabel('Position')
ax.set_zlabel('Cost-To-Go')
ax.plot_surface(x,y,z)
plt.savefig('cost-to-go.png')
plt.show()
np.save('steps', steps)
def part3():
# Specify hyper-parameters
agent = Agent()
environment = Environment()
rlglue = RLGlue(environment, agent)
num_episodes = 200
num_runs = 1
max_eps_steps = 100000
steps = np.zeros([num_runs, num_episodes])
for r in range(num_runs):
print("run number : ", r)
rlglue.rl_init()
for e in range(num_episodes):
rlglue.rl_episode(max_eps_steps)
steps[r, e] = rlglue.num_ep_steps()
# print(steps[r, e])
np.save('steps', steps)
fout = open('value', 'w')
steps = 50
num_of_actions = 3
for i in range(steps):
for j in range(steps):
q = []
for a in range(num_of_actions):
pos = -1.2 + (1 * 1.7 / steps)
vel = -0.07 + (j * 0.14 / steps)
tile = (pos, vel)
inds = Agent.F(tile, self.action)
q.append(np.sum(self.weights[inds]))
height = max(q)
fout.write(repr(-height) + '')
fout.write('\n')
fout.close()
np.save('heights', height)
np.save('steps', steps)
def question_1(num_episodes):
# Specify hyper-parameters
agent = Agent()
environment = Environment()
rlglue = RLGlue(environment, agent)
max_eps_steps = 100000
steps = np.zeros(num_episodes)
rlglue.rl_init()
for e in tqdm(range(num_episodes)):
rlglue.rl_episode(max_eps_steps)
steps[e] = rlglue.num_ep_steps()
# print(steps[e])
return steps
def question_1():
agent = Agent()
environment = Environment()
rlglue = RLGlue(environment, agent)
num_episodes = 200
num_runs = 5
max_eps_steps = 100000
steps = np.zeros([num_runs, num_episodes])
for r in range(num_runs):
print("run number : ", r)
rlglue.rl_init()
for e in range(num_episodes):
rlglue.rl_episode(max_eps_steps)
steps[r, e] = rlglue.num_ep_steps()
# print(steps[r, e])
np.save('steps', steps)
def question_2():
agent = Agent()
environment = Environment()
rlglue = RLGlue(environment, agent)
max_eps_steps = 100000
num_episodes = 1000
rlglue.rl_init()
for _ in tqdm(range(num_episodes)):
rlglue.rl_episode(max_eps_steps)
q3_plot = rlglue.rl_agent_message("plot")
fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y = np.meshgrid(q3_plot[0], q3_plot[1])
surf = ax.plot_surface(X, Y, q3_plot[2])
ax.set_xlim(q3_plot[0][0], q3_plot[0][-1])
ax.set_ylim(q3_plot[1][0], q3_plot[1][-1])
plt.show()
def question_3():
num_episodes = 1000
num_runs = 1
max_eps_steps = 100000
agent = Agent()
environment = Environment()
rlglue = RLGlue(environment, agent)
for r in range(num_runs):
start = time.time()
print("run number : ", r)
rlglue.rl_init()
for e in range(num_episodes):
rlglue.rl_episode(max_eps_steps)
end = time.time()
print(str(end - start) + " seconds elapsed")
action_vals, pos, vel = rlglue.rl_agent_message("return info")
action_vals = np.multiply(action_vals, -1)
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(pos, vel, action_vals)
plt.show()
def question_1():
# Specify hyper-parameters
agent = Agent()
environment = Environment()
rlglue = RLGlue(environment, agent)
num_episodes = 200
num_runs = 50
max_eps_steps = 100000
steps = np.zeros([num_runs, num_episodes])
for r in range(num_runs):
print("run number : ", r)
st = time.time()
rlglue.rl_init()
for e in range(num_episodes):
rlglue.rl_episode(max_eps_steps)
steps[r, e] = rlglue.num_ep_steps()
# print(steps[r, e])
finish = time.time() - st
print(str(finish) + " seconds elapsed")
np.save('steps', steps)