def cartpole():
model = InvertedPendulum()
observation_space = 4
action_space = 1
dqn_solver = DQNSolver(observation_space, action_space)
run = 0
while True:
run += 1
state = model.state
state = np.reshape(state, [1, observation_space])
step = 0
while True:
step += 1
action = dqn_solver.act(state)
state_next, reward, terminal, info = model.step_rl(action)
reward = reward if not terminal else -reward
state_next = np.reshape(state_next, [1, observation_space])
dqn_solver.remember(state, action, reward, state_next, terminal)
state = state_next
if terminal:
print("Run: " + str(run) + ", exploration: " +
str(dqn_solver.exploration_rate) + ", score: " +
str(step))
break
dqn_solver.experience_replay()
from src.InvertedPendulum import *
from src.Simulator import *
from src.controllers.PolePlacementController import *
# This script shows the behavior of the pendulum controlled by a pole placement controller
if __name__ == "__main__":
# Import model
model = InvertedPendulum()
# Set initial state [m, m/s, rad, rad/s]
model.set_state(np.mat([[-0.3], [0.0], [0.1], [0.0]]))
# Define controller
controller = Control(model)
# Set desired cart position (m)
controller.set_desired_position(0.3)
# Simulator
sim = Simulator(model, controller)
t, state_list = sim.simulate()
# Plot data
plt.figure()
plt.subplot(2, 2, 1)
plt.plot(t, state_list[:, 0])
plt.xlabel("Time (s)")
plt.ylabel("x (m)")
plt.title("Cart position")
from src.InvertedPendulum import *
import casadi as ca
import matplotlib.pyplot as plt
# Trajectory parameters
T = 2
N = 25
hk = T / N
umax = 20
dmax = 2
d = 1
# Import pendulum model
model = InvertedPendulum()
# Initial state
x0 = [0, 0, 0, 0]
xd = [0, 0, -pi / 4, 0]
# Define decision variables
u = ca.SX.sym('u', 1, N)
x = ca.SX.sym('x', 4, N)
w = ca.vertcat(u, x)
# Define objective
cost = 0
for k in range(N - 1):
cost += (hk / 2) * (u[:, k]**2 + u[:, k + 1]**2)
# Create collocation constraints
constr = []
ax1.grid()
# Trajectory parameters
T = 2
N = 25
hk = T / N
umax = 20
dmax = 2
d = 1
# Initial state
x0 = [0, 0, -pi / 4, 0]
xd = [1, 0, 0, 0]
# Import pendulum model
model = InvertedPendulum()
model.set_state(np.reshape(x0, (4, 1)))
model.dt = hk
# Define decision variables
u = cvxpy.Variable((1, N))
x = cvxpy.Variable((4, N))
# Define objective
cost = 0
for k in range(N - 1):
cost += (hk / 2) * (u[:, k]**2 + u[:, k + 1]**2)
objective = cvxpy.Minimize(cost)
# Create collocation constraints
constr = []