def get_gridworld():
gridworld = EnvBaseline(name='Sample Grid World',
s_hash_rowL=s_hash_rowL,
row_tickL=row_tickL,
x_axis_label=x_axis_label,
col_tickL=col_tickL,
y_axis_label=y_axis_label,
colorD={
'Goal': 'g',
'Pit': 'r',
'Start': 'b'
},
basic_color='skyblue')
gridworld.set_info('Sample Grid World showing basic MDP creation.')
# add actions from each state
# (note: a_prob will be normalized within add_action_dict)
gridworld.add_action_dict(actionD)
# for each action, define the next state and transition probability
# (here we use the layout definition to aid the logic)
for s_hash, aL in actionD.items():
for a_desc in aL:
sn_hash = get_next_state(s_hash, a_desc)
reward = rewardD.get(sn_hash, 0.0)
# for deterministic MDP, use t_prob=1.0
gridworld.add_transition(s_hash,
a_desc,
sn_hash,
t_prob=1.0,
reward_obj=reward)
# after the "add" commands, send all states and actions to environment
# (any required normalization is done here as well.)
gridworld.define_env_states_actions()
# If there is a start state, define it here.
gridworld.start_state_hash = 'Start'
# If a limited number of start states are desired, define them here.
gridworld.define_limited_start_state_list([(2, 0), (2, 2)])
# if a default policy is desired, define it as a dict.
gridworld.default_policyD = {
(0, 0): 'R',
(1, 0): 'U',
(0, 1): 'R',
(0, 2): 'R',
(1, 2): 'U',
'Start': 'U',
(2, 2): 'U',
(2, 1): 'R',
(2, 3): 'L'
}
return gridworld
def get_gridworld(step_reward=0.0):
gridworld = EnvBaseline(
name='Simple Grid World') # GenericLayout set below
gridworld.set_info('Simple Grid World Example.')
actionD = {
(0, 0): ('D', 'R'),
(0, 1): ('L', 'R'),
(0, 2): ('L', 'D', 'R'),
(1, 0): ('U', 'D'),
(1, 2): ('U', 'D', 'R'),
(2, 0): ('U', 'R'),
(2, 1): ('L', 'R'),
(2, 2): ('L', 'R', 'U'),
(2, 3): ('L', 'U')
}
rewardD = {(0, 3): 1, (1, 3): -1}
for state_hash, actionL in actionD.items():
for action_desc in actionL:
gridworld.add_action(state_hash, action_desc,
a_prob=1.0) # a_prob will be normalized
a = action_desc
s = state_hash
if a == 'U':
state_next_hash = (s[0] - 1, s[1])
elif a == 'D':
state_next_hash = (s[0] + 1, s[1])
elif a == 'R':
state_next_hash = (s[0], s[1] + 1)
elif a == 'L':
state_next_hash = (s[0], s[1] - 1)
reward_val = rewardD.get(state_next_hash, step_reward)
gridworld.add_transition(state_hash,
action_desc,
state_next_hash,
t_prob=1.0,
reward_obj=reward_val)
gridworld.define_env_states_actions(
) # send all states and actions to environment
gridworld.layout = GenericLayout(
gridworld) # uses default "get_layout_row_col_of_state"
# If there is a start state, define it here.
gridworld.start_state_hash = (2, 0)
gridworld.define_limited_start_state_list([(2, 0), (2, 2)])
# define default policy (if any)
# Policy Dictionary for: GridWorld
policyD = {} # index=state_hash, value=action_desc
# Vpi shown for gamma=0.9
policyD[(0, 0)] = 'R' # Vpi=0.81
policyD[(1, 0)] = 'U' # Vpi=0.729
policyD[(0, 1)] = 'R' # Vpi=0.9
policyD[(0, 2)] = 'R' # Vpi=1.0
policyD[(1, 2)] = 'U' # Vpi=0.9
policyD[(2, 0)] = 'U' # Vpi=0.6561
policyD[(2, 2)] = 'U' # Vpi=0.81
policyD[(2, 1)] = 'R' # Vpi=0.729
policyD[(2, 3)] = 'L' # Vpi=0.729
gridworld.default_policyD = policyD
return gridworld
