continuous.py File Reference

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Classes
class	continuous.PolicyNetwork
class	continuous.QValueNetwork
class	continuous.ReplayItem

Namespaces
	continuous

Functions
def	continuous.rendertrial (maxiter=NSTEPS, verbose=True)

Variables
	continuous.batch
int	continuous.BATCH_SIZE = 64
	continuous.d_batch = np.vstack([b.done for b in batch])
float	continuous.DECAY_RATE = 0.99
bool	continuous.done = False
	continuous.env = Pendulum(1)
	continuous.feed_dict
list	continuous.h_qva = []
list	continuous.h_rwd = []
list	continuous.h_ste = []
tuple	continuous.maxq
	continuous.n_init = tflearn.initializations.truncated_normal(seed=RANDOM_SEED)
int	continuous.NEPISODES = 100
int	continuous.NH1 = 250
int	continuous.NSTEPS = 100
	continuous.NU = env.nu
	continuous.NX = env.nobs
	continuous.optim
	continuous.policy = PolicyNetwork().setupOptim()
float	continuous.POLICY_LEARNING_RATE = 0.0001
	continuous.policyTarget = PolicyNetwork().setupTargetAssign(policy)
	continuous.q2_batch
	continuous.qgrad
	continuous.qref_batch = r_batch + (not d_batch) * (DECAY_RATE * q2_batch)
	continuous.qvalue = QValueNetwork().setupOptim()
float	continuous.QVALUE_LEARNING_RATE = 0.001
	continuous.qvalueTarget = QValueNetwork().setupTargetAssign(qvalue)
	continuous.r
	continuous.r_batch = np.vstack([b.reward for b in batch])
	continuous.RANDOM_SEED = int((time.time() % 10) * 1000)
int	continuous.REPLAY_SIZE = 10000
	continuous.replayDeque = deque()
float	continuous.rsum = 0.0
	continuous.sess = tf.InteractiveSession()
	continuous.u = sess.run(policy.policy, feed_dict={policy.x: x})
	continuous.u2_batch
	continuous.u_batch = np.vstack([b.u for b in batch])
	continuous.u_init = tflearn.initializations.uniform(minval=-0.003, maxval=0.003, seed=RANDOM_SEED)
	continuous.u_targ = sess.run(policy.policy, feed_dict={policy.x: x_batch})
float	continuous.UPDATE_RATE = 0.01
	continuous.withSinCos
	continuous.x = env.reset().T
	continuous.x2 = x2.T
	continuous.x2_batch = np.vstack([b.x2 for b in batch])
	continuous.x_batch = np.vstack([b.x for b in batch])