Classes | |
class | PolicyNetwork |
class | QValueNetwork |
class | ReplayItem |
Functions | |
rendertrial (maxiter=NSTEPS, verbose=True) | |
Variables | |
batch | |
int | BATCH_SIZE = 64 |
d_batch = np.vstack([b.done for b in batch]) | |
float | DECAY_RATE = 0.99 |
bool | done = False |
env = Pendulum(1) | |
feed_dict | |
list | h_qva = [] |
list | h_rwd = [] |
list | h_ste = [] |
tuple | maxq |
n_init = tflearn.initializations.truncated_normal(seed=RANDOM_SEED) | |
int | NEPISODES = 100 |
int | NH1 = 250 |
int | NSTEPS = 100 |
NU = env.nu | |
NX = env.nobs | |
optim | |
policy = PolicyNetwork().setupOptim() | |
float | POLICY_LEARNING_RATE = 0.0001 |
policyTarget = PolicyNetwork().setupTargetAssign(policy) | |
q2_batch | |
qgrad | |
qref_batch = r_batch + (not d_batch) * (DECAY_RATE * q2_batch) | |
qvalue = QValueNetwork().setupOptim() | |
float | QVALUE_LEARNING_RATE = 0.001 |
qvalueTarget = QValueNetwork().setupTargetAssign(qvalue) | |
r | |
r_batch = np.vstack([b.reward for b in batch]) | |
RANDOM_SEED = int((time.time() % 10) * 1000) | |
int | REPLAY_SIZE = 10000 |
replayDeque = deque() | |
float | rsum = 0.0 |
sess = tf.InteractiveSession() | |
u = sess.run(policy.policy, feed_dict={policy.x: x}) | |
u2_batch | |
u_batch = np.vstack([b.u for b in batch]) | |
u_init = tflearn.initializations.uniform(minval=-0.003, maxval=0.003, seed=RANDOM_SEED) | |
u_targ = sess.run(policy.policy, feed_dict={policy.x: x_batch}) | |
float | UPDATE_RATE = 0.01 |
withSinCos | |
x = env.reset().T | |
x2 = x2.T | |
x2_batch = np.vstack([b.x2 for b in batch]) | |
x_batch = np.vstack([b.x for b in batch]) | |
Deep actor-critic network, From "Continuous control with deep reinforcement learning", by Lillicrap et al, arXiv:1509.02971
rendertrial | ( | maxiter = NSTEPS , |
|
verbose = True |
|||
) |
Definition at line 157 of file continuous.py.
batch |
Definition at line 204 of file continuous.py.
int BATCH_SIZE = 64 |
Definition at line 35 of file continuous.py.
d_batch = np.vstack([b.done for b in batch]) |
Definition at line 210 of file continuous.py.
float DECAY_RATE = 0.99 |
Definition at line 32 of file continuous.py.
bool done = False |
Definition at line 190 of file continuous.py.
env = Pendulum(1) |
Definition at line 39 of file continuous.py.
feed_dict |
Definition at line 226 of file continuous.py.
list h_qva = [] |
Definition at line 176 of file continuous.py.
list h_rwd = [] |
Definition at line 175 of file continuous.py.
list h_ste = [] |
Definition at line 177 of file continuous.py.
tuple maxq |
Definition at line 250 of file continuous.py.
n_init = tflearn.initializations.truncated_normal(seed=RANDOM_SEED) |
Definition at line 24 of file continuous.py.
int NEPISODES = 100 |
Definition at line 28 of file continuous.py.
int NH1 = 250 |
Definition at line 36 of file continuous.py.
int NSTEPS = 100 |
Definition at line 29 of file continuous.py.
NU = env.nu |
Definition at line 42 of file continuous.py.
NX = env.nobs |
Definition at line 41 of file continuous.py.
optim |
Definition at line 240 of file continuous.py.
policy = PolicyNetwork().setupOptim() |
Definition at line 143 of file continuous.py.
float POLICY_LEARNING_RATE = 0.0001 |
Definition at line 31 of file continuous.py.
policyTarget = PolicyNetwork().setupTargetAssign(policy) |
Definition at line 144 of file continuous.py.
q2_batch |
Definition at line 217 of file continuous.py.
qgrad |
Definition at line 235 of file continuous.py.
qref_batch = r_batch + (not d_batch) * (DECAY_RATE * q2_batch) |
Definition at line 221 of file continuous.py.
qvalue = QValueNetwork().setupOptim() |
Definition at line 146 of file continuous.py.
float QVALUE_LEARNING_RATE = 0.001 |
Definition at line 30 of file continuous.py.
qvalueTarget = QValueNetwork().setupTargetAssign(qvalue) |
Definition at line 147 of file continuous.py.
r |
Definition at line 188 of file continuous.py.
r_batch = np.vstack([b.reward for b in batch]) |
Definition at line 209 of file continuous.py.
RANDOM_SEED = int((time.time() % 10) * 1000) |
Definition at line 19 of file continuous.py.
int REPLAY_SIZE = 10000 |
Definition at line 34 of file continuous.py.
replayDeque = deque() |
Definition at line 139 of file continuous.py.
float rsum = 0.0 |
Definition at line 182 of file continuous.py.
sess = tf.InteractiveSession() |
Definition at line 149 of file continuous.py.
u = sess.run(policy.policy, feed_dict={policy.x: x}) |
Definition at line 186 of file continuous.py.
u2_batch |
Definition at line 214 of file continuous.py.
u_batch = np.vstack([b.u for b in batch]) |
Definition at line 208 of file continuous.py.
u_init = tflearn.initializations.uniform(minval=-0.003, maxval=0.003, seed=RANDOM_SEED) |
Definition at line 25 of file continuous.py.
u_targ = sess.run(policy.policy, feed_dict={policy.x: x_batch}) |
Definition at line 234 of file continuous.py.
float UPDATE_RATE = 0.01 |
Definition at line 33 of file continuous.py.
withSinCos |
Definition at line 40 of file continuous.py.
x = env.reset().T |
Definition at line 181 of file continuous.py.
x2 = x2.T |
Definition at line 188 of file continuous.py.
x2_batch = np.vstack([b.x2 for b in batch]) |
Definition at line 211 of file continuous.py.
x_batch = np.vstack([b.x for b in batch]) |
Definition at line 207 of file continuous.py.