def reset(self):
"""
Description
-----
Resets the PID input trajectory.
"""
self.u = []
if __name__ == "__main__":
# Build RL Objects
rl = ReinforceLearning(discount_factor=0.95, states_start=300, states_stop=340, states_interval=0.5,
actions_start=-15, actions_stop=15, actions_interval=2.5, learning_rate=0.5,
epsilon=0.2, doe=1.2, eval_period=30)
# Building states for the problem, states will be the tracking errors
states = np.linspace(-15, 25, 201)
rl.user_states(list(states))
# Building actions for the problem, actions will be inputs of u2
actions = np.linspace(-12, 18, 121)
rl.user_actions(actions)
# Load Q, T, and NT matrices from previous training
q = np.loadtxt("Q_Matrix.txt")
t = np.loadtxt("T_Matrix.txt")
-----
Resets the PID input trajectory.
"""
self.u = []
if __name__ == "__main__":
# Build RL Objects
rl = ReinforceLearning(discount_factor=0.95,
states_start=300,
states_stop=340,
states_interval=0.5,
actions_start=-15,
actions_stop=15,
actions_interval=2.5,
learning_rate=0.5,
epsilon=0.2,
doe=1.2,
eval_period=30)
# Building states for the problem, states will be the tracking errors
states = []
rl.x1 = np.linspace(-10, 10, 21)
rl.x2 = np.linspace(-10, 10, 21)
for x1 in rl.x1:
for x2 in rl.x2:
states.append([x1, x2])
-----
Resets the PID input trajectory.
"""
self.u = []
if __name__ == "__main__":
# Build RL Objects
rl = ReinforceLearning(discount_factor=0.95,
states_start=300,
states_stop=340,
states_interval=0.5,
actions_start=-15,
actions_stop=15,
actions_interval=2.5,
learning_rate=0.5,
epsilon=0.2,
doe=1.2,
eval_period=60)
# Building states for the problem, states will be the tracking errors
states = []
rl.x1 = np.linspace(-15, 15, 121)
rl.x2 = np.linspace(-5, 5, 41)
for i in rl.x1:
for j in rl.x2:
states.append([i, j])