def choose_action(self, env, state):
if self.room != state.r:
self.room = state.r
self.plan = None
pos = (state.x, state.y)
if self.plan is None or pos not in self.plan:
priority_func = lambda s: manhattan_dist(s, env.goal)
expand_partial = lambda s: expand_state(env.states, s)
self.plan = search.best_first_graph_search(pos, env.goal, priority_func, expand_partial)
for i, pathpos in enumerate(self.plan):
if i == len(self.plan)-1:
#print 'choose random option in door plan'
return np.array([choice(env.actions)])
elif pos == pathpos:
fx,fy = self.plan[i+1]
dx,dy = (fx-state.x, fy-state.y)
#print 'move in direction',dx,dy,'for door plan'
return np.array([env.movemap[dx,dy]])
def choose_action_parameterized(self, env, state, field, action):
if self.room != state.r:
self.room = state.r
self.plan = None
pos = (state.x, state.y)
if self.plan is None or pos not in self.plan:
key_pos = filter_states(env.states, field)
# Not positive if this is right move here.
if not key_pos:
#print 'RANDOM action for plan', action
return np.array([choice(env.actions)])
priority_func = lambda s: manhattan_dist(s, key_pos[0])
expand_partial = lambda s: expand_state(env.states, s)
self.plan = search.best_first_graph_search(pos, key_pos[0], priority_func, expand_partial)
for i, pathpos in enumerate(self.plan):
if i == len(self.plan)-1:
#print 'action', action, 'for plan', field
return np.array([action])
elif pos == pathpos:
fx,fy = self.plan[i+1]
dx,dy = (fx-state.x, fy-state.y)
#print 'move', dx,dy, 'for plan', field
return np.array([env.movemap[dx,dy]])