# units
L =  1.00000000 (length)  # in the text this is \sigma
tau =  1.00000000 (time)
# free parameters
Pe = 100.00000000 (Peclet)
ell =  100.00000000 (persistence)
mu = 1.0 (motility)			# not used in this problem but left because you never know...
R =  0.5000000 (radius of the target)
# other parameters
dt =  0.00010000 (integration time step)
# PS parameters
N runs =       100 (number of independent runs)
N episodes =   900 (number of episodes for each run)
N episodes0 =   100  (restart from this episode)
N ep_last =   10 (saving all data -not policy- in the last this many episodes)
T = 1.00000000 (duration of a single episode)
Dr = 0.01 (binning of distances to discretize the state space)
R_max = 10.0 (maximum distance for states)
rew =  1.00000000 (reward when a target is found)
gammma =  0.0000010 (gammma)
eta =  0.00010000 (eta)
constant initial policy = 1
pBP =  0.01000000 (initial probability of changing when passive)
pABP =  0.00100000 (initial probability of changing when active)
r_ini = 5.0 (initial radius when using initial radius codes)