) # send all states and actions to environment
gridworld.start_state_hash = (0, 0)
return gridworld
if __name__ == "__main__": # pragma: no cover
from introrl.dp_funcs.dp_value_iter import dp_value_iteration
gridworld = get_gridworld()
#gridworld.summ_print()
gridworld.layout_print(vname='reward',
fmt='',
show_env_states=True,
none_str='*')
gridworld.save_to_pickle_file(fname=None)
policy, state_value = dp_value_iteration(gridworld,
do_summ_print=True,
max_iter=1000,
err_delta=0.001,
gamma=0.9)
policy.save_diagram(gridworld,
inp_colorD=None,
save_name='sutton_5x5_gridworld',
show_arrows=True,
scale=1.0,
h_over_w=0.8)
get_sim = Model( RW, build_initial_model=True )
get_sim.collect_transition_data( num_det_calls=100, num_stoic_calls=10000 )
RW.layout.s_hash_print()
#get_sim.num_calls_layout_print()
#get_sim.min_num_calls_layout_print()
env = EnvBaseline( s_hash_rowL=RW.s_hash_rowL,
x_axis_label=RW.x_axis_label,
y_axis_label=RW.y_axis_label )
get_sim.add_all_data_to_an_environment( env )
policy, state_value = dp_value_iteration( env, do_summ_print=True, fmt_V='%.3f', fmt_R='%.1f',
max_iter=1000, err_delta=0.0001,
gamma=0.9, iteration_prints=10)
policy.save_diagram( RW, inp_colorD=None, save_name='dp_rw1000_policy',
show_arrows=False, scale=0.5, h_over_w=0.8,
show_terminal_labels=False)
print( 'Total Time =',time.time() - start_time )
pickle_esp.save_to_pickle_file( fname='dp_soln_to_randwalk_1000',
env=env, state_values=state_value, policy=policy)
from introrl.dp_funcs.dp_value_iter import dp_value_iteration
from introrl.environments.env_baseline import EnvBaseline
from introrl.mdp_data.sutton_5x5_gridworld import get_gridworld
gridworld = get_gridworld()
gridworld.name = 'Figure 3.5, 5x5 Grid Value Iteration'
policy, state_value = dp_value_iteration( gridworld, do_summ_print=True,fmt_V='%.1f',
max_iter=1000, err_delta=0.001,
gamma=0.9, allow_multi_actions=True)
policy.save_diagram( gridworld, inp_colorD=None, save_name='figure_3_5_policy',
show_arrows=True, scale=0.8, h_over_w=0.8, do_show=True)
from introrl.mdp_data.fallen_3state_robot import get_robot
robot = get_robot()
do_VI = 0
if do_VI:
print('_____________ Value Iteration ________________')
else:
print('_____________ Policy Iteration ________________')
for gamma in (0.001, 0.01, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99, 0.999):
if do_VI:
policy, sv = dp_value_iteration(robot,
do_summ_print=False,
fmt_V='%.1f',
max_iter=1000,
err_delta=0.001,
gamma=gamma)
else:
policy = Policy(environment=robot)
policy.set_policy_from_piD(robot.get_default_policy_desc_dict())
sv = StateValues(robot)
sv.init_Vs_to_zero()
dp_policy_iteration(policy,
sv,
do_summ_print=False,
max_iter=1000,
err_delta=0.001,
import matplotlib
import matplotlib.pyplot as plt
from introrl.dp_funcs.dp_value_iter import dp_value_iteration
from introrl.mdp_data.gamblers_problem import get_gambler
gambler = get_gambler(prob_heads=0.4)
policy, state_value = dp_value_iteration(gambler,
allow_multi_actions=True,
do_summ_print=True,
fmt_V='%.4f',
max_iter=1000,
err_delta=0.00001,
gamma=1.0)
print(gambler.get_info())
# --------------- plot logic -------------------
min_state_list = []
min_action_list = []
state_list = []
action_list = []
for i_state in range(1, 100):
aL = policy.get_list_of_all_action_desc_prob(i_state, incl_zero_prob=False)
min_state_list.append(i_state - 0.5)
min_action_list.append(min([a for a, p in aL]))
min_state_list.append(i_state + 0.5)
min_action_list.append(min([a for a, p in aL]))
