qtable Namespace Reference

Functions
def	rendertrial (maxiter=100)

Variables
float	DECAY_RATE = 0.99
	env = DPendulum()
	— Environment
list	h_rwd = []
float	LEARNING_RATE = 0.85
int	NEPISODES = 500
	— Hyper paramaters
int	NSTEPS = 50
	NU = env.nu
	NX = env.nx
	Q = np.zeros([env.nx,env.nu])
float	Qref = reward + DECAY_RATE*np.max(Q[x2,:])
	RANDOM_SEED = int((time.time()%10)*1000)
	— Random seed
	reward
float	rsum = 0.0
	u = np.argmax(Q[x,:] + np.random.randn(1,NU)/episode)
	x = env.reset()
	x2

Detailed Description

Example of Q-table learning with a simple discretized 1-pendulum environment.

Function Documentation

rendertrial()

def qtable.rendertrial

(

maxiter = 100

)

Roll-out from random state using greedy policy.

Definition at line 29 of file qtable.py.