def post_step(self, world, act):
t = world.getState(WORLD, 'seconds', unique=True)
if len(self.model_data) == 0 or self.model_data[-1]['Timestep'] != t:
# Haven't made some inference for this timestep (maybe wait until last one?)
player_name = self.player_name()
player = world.agents[player_name]
agent = world.agents['ATOMIC']
# Store beliefs over player models
beliefs = agent.getBelief()
if len(beliefs) > 1:
raise RuntimeError('Agent {} has {} possible models in true state'.format(agent.name, len(beliefs)))
beliefs = next(iter(beliefs.values()))
player_model = world.getFeature(modelKey(player_name), beliefs)
for model in player_model.domain():
entry = {'Timestep': t, 'Belief': player_model[model]}
# Find root model (i.e., remove the auto-generated numbers from the name)
while player.models[player.models[model]['parent']]['parent'] is not None:
model = player.models[model]['parent']
entry['Model'] = model[len(player_name) + 1:]
self.model_data.append(entry)
if self.condition_dist:
condition_dist = Distribution()
for model, model_prob in player_model.items():
for condition, condition_prob in self.condition_dist[model_to_cluster(model)].items():
condition_dist.addProb(condition, model_prob*condition_prob)
condition_dist.normalize()
for condition, condition_prob in condition_dist.items():
self.condition_data.append({'Timestep': t, 'Belief': condition_prob, 'Condition': condition})
def next_victim(self, world):
"""
Generate an expectation about what room the player will enter next
"""
player = world.agents[self.parser.player_name()]
action = world.getAction(player.name, unique=True)
if action['verb'] == 'triage_Green':
# Triaging green as we speak
return Distribution({'Green': 1})
elif action['verb'] == 'triage_Gold':
# Triaging yellow as we speak
return Distribution({'Yellow': 1})
# Not so obvious who will be next
agent = world.agents['ATOMIC']
beliefs = agent.getBelief()
if len(beliefs) == 1:
agent_model, agent_beliefs = next(iter(beliefs.items()))
else:
raise NotImplementedError(
'Unable to generate predictions unless agent has unique model')
location = world.getState(player.name, 'loc', unique=True)
prediction = None
for player_model, player_model_prob in world.getModel(
player.name, agent_beliefs).items():
player_beliefs = player.models[player_model]['beliefs']
fov = world.getState(player.name,
'vicInFOV',
player_beliefs,
unique=True)
if fov in {'Yellow', 'Green'}:
# The next victim found is the one the player is looking at now
next_seen = Distribution({fov: 1})
else:
# The next victim found is one in the player's current location
next_seen = {
'Yellow':
world.getState(WORLD, 'ctr_{}_Gold'.format(location),
player_beliefs).expectation(),
'Green':
world.getState(WORLD, 'ctr_{}_Green'.format(location),
player_beliefs).expectation()
}
if sum(next_seen.values()) == 0:
# No victim in the current room
next_seen = {'Yellow': 1, 'Green': 1}
next_seen = Distribution(next_seen)
next_seen.normalize()
if prediction is None:
prediction = next_seen.scale_prob(player_model_prob)
else:
prediction = prediction.__class__({
color: prob + next_seen[color] * player_model_prob
for color, prob in prediction.items()
})
return prediction