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authordamic014 <damic014@umn.edu>2019-11-26 19:31:51 -0600
committerdamic014 <damic014@umn.edu>2019-11-26 19:31:51 -0600
commitc71b351c164a1fb86b5efe0501d4ca40f1e33949 (patch)
tree5de439098f7287d7e9c3f466dd33385abcf137cd /System/system_swingup_test_2.py
parentMerge remote-tracking branch 'origin/library_interrupt_flags' (diff)
parentGot threading and SW limits working really well. Still might be some shakines... (diff)
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Merge remote-tracking branch 'origin/library_encoder_thread'
Diffstat (limited to 'System/system_swingup_test_2.py')
-rw-r--r--System/system_swingup_test_2.py34
1 files changed, 25 insertions, 9 deletions
diff --git a/System/system_swingup_test_2.py b/System/system_swingup_test_2.py
index 81d5419..5c0ac5a 100644
--- a/System/system_swingup_test_2.py
+++ b/System/system_swingup_test_2.py
@@ -53,13 +53,13 @@ class SwingUpEnv():
}
def __init__(self):
- self.sys = System(angular_units='Radians')
+ self.x_threshold = 14.
+ self.sys = System(angular_units='Radians', positive_limit=self.x_threshold, negative_limit=-self.x_threshold, sw_limit_routine=self.x_threshold_routine)
- self.force_mag = 10.
+ self.force_mag = 11.
self.last_time = time.time() # time for seconds between updates
# Angle at which to fail the episode
- self.x_threshold = 10.
self.x_dot_threshold = 10.
self.theta_dot_threshold = 3*np.pi
@@ -73,6 +73,7 @@ class SwingUpEnv():
self.state = None
self.steps_beyond_done = None
+ self.done = False
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
@@ -83,7 +84,9 @@ class SwingUpEnv():
state = self.state
x, x_dot, theta, theta_dot = state
force = self.force_mag * action[0]
- self.sys.adjust(force)
+ # Do not adjust the motor further if the x_threshold has been triggered by the SW limit
+ if self.done == False:
+ self.sys.adjust(force)
costheta = math.cos(theta)
sintheta = math.sin(theta)
@@ -105,10 +108,15 @@ class SwingUpEnv():
self.state = (new_x, x_dot, new_theta, theta_dot)
self.sys.add_results(new_theta, new_x, force)
- done = x < -self.x_threshold \
+ done = theta_dot < -self.theta_dot_threshold \
+ or theta_dot > self.theta_dot_threshold \
+ or self.done == True
+
+ '''done = x < -self.x_threshold \
or x > self.x_threshold \
or theta_dot < -self.theta_dot_threshold \
- or theta_dot > self.theta_dot_threshold
+ or theta_dot > self.theta_dot_threshold \
+ or self.done == True'''
done = bool(done)
if not done:
@@ -125,6 +133,11 @@ class SwingUpEnv():
return np.array(self.state), reward, done, {'max_up_time' : self.max_up_time}
+ def x_threshold_routine(self):
+ print("SW Limit reached!")
+ self.done = True
+ self.sys.adjust(0)
+
def reset(self, home = True):
if home == True:
self.sys.return_home()
@@ -138,6 +151,7 @@ class SwingUpEnv():
self.max_up_time = 0
self.up = False
self.steps_beyond_done = None
+ self.done = False
return np.array(self.state)
def end(self):
@@ -298,14 +312,16 @@ class sarsaAgent:
env = SwingUpEnv()
# For simplicity, we only consider forces of -1 and 1
-numActions = 5
+numActions = 2
Actions = np.linspace(-1, 1, numActions)
# This is our learning agent
gamma = .95
-agent = sarsaAgent(5, numActions, 20, 1, epsilon = 5e-2, gamma = gamma, alpha = 1e-5)
+#agent = sarsaAgent(5, numActions, 20, 1, epsilon = 5e-2, gamma = gamma, alpha = 1e-5)
+agent = deepQagent(5,numActions,20,2,epsilon=5e-2,gamma=gamma,batch_size=20,
+ c= 100,alpha=1e-4)
-maxSteps = 5e4
+maxSteps = 2e5
# This is a helper to deal with the fact that x[2] is actually an angle
x_to_y = lambda x : np.array([x[0], x[1], np.cos(x[2]), np.sin(x[2]), x[3]])