def decide(self, ent, stim, isDead, n_dead=0):
entID, annID = ent.entID, ent.annID
reward = self.config.STEPREWARD + self.config.DEADREWARD * n_dead
stim_tensor = torchlib.Stim(ent, stim, self.config)
outsLm = self.lawmaker(stim_tensor.flat.view(1, -1),
stim_tensor.ents.unsqueeze(0), isDead, annID)
annReturns = self.anns[annID](stim_tensor.flat.view(1, -1),
stim_tensor.ents.unsqueeze(0), outsLm,
isDead)
playerActions, actionTargets, actionDecisions = self.getActionArguments(
annReturns, stim, ent)
moveAction = int(annReturns['actions']['move'])
attack = actionDecisions.get('attack', None)
if moveAction > 4:
moveAction -= 5
ent.moveDec = moveAction
contact = int(attack is not None)
Asw = -np.mean([float(t.mean()) for t in outsLm['Qs'].values()])
outsLm = self.lawmaker.get_punishment(outsLm, annReturns['actions'])
Asw += np.mean([float(t) for t in outsLm['Qs'].values()])
self.collectStep(entID, annReturns['actions'], annReturns['policy'],
stim_tensor.flat.numpy(), stim_tensor.ents.numpy(),
reward, contact, float(annReturns['val']))
self.collectStepLm(entID, outsLm['actions'], outsLm['policy'])
if not self.config.TEST:
self.updates[entID].feather.scrawl(ent, float(annReturns['val']),
reward, Asw, attack, contact)
return playerActions, actionTargets
def visVals(self, food='max', water='max'):
posList, vals = [], []
R, C = self.world.shape
for r in range(self.config.BORDER, R - self.config.BORDER):
for c in range(self.config.BORDER, C - self.config.BORDER):
colorInd = int(12 * np.random.rand())
color = Neon.color12()[colorInd]
color = (colorInd, color)
ent = entity.Player(-1, color, self.config)
ent._pos = (r, c)
if food != 'max':
ent._food = food
if water != 'max':
ent._water = water
posList.append(ent.pos)
self.world.env.tiles[r, c].addEnt(ent.entID, ent)
stim = self.world.env.stim(ent.pos, self.config.STIM)
s = torchlib.Stim(ent, stim, self.config)
val = self.valNet(s).detach()
self.world.env.tiles[r, c].delEnt(ent.entID)
vals.append(float(val))
vals = list(zip(posList, vals))
return vals
def visDeps(self):
from forge.blade.core import realm
from forge.blade.core.tile import Tile
colorInd = int(12 * np.random.rand())
color = Neon.color12()[colorInd]
color = (colorInd, color)
ent = realm.Desciple(-1, self.config, color).server
targ = realm.Desciple(-1, self.config, color).server
sz = 15
tiles = np.zeros((sz, sz), dtype=object)
for r in range(sz):
for c in range(sz):
tiles[r, c] = Tile(enums.Grass, r, c, 1, None)
targ.pos = (7, 7)
tiles[7, 7].addEnt(0, targ)
posList, vals = [], []
for r in range(sz):
for c in range(sz):
ent.pos = (r, c)
tiles[r, c].addEnt(1, ent)
s = torchlib.Stim(ent, tiles, self.config)
conv, flat, ents = s.conv, s.flat, s.ents
val = self.valNet(s)
vals.append(float(val))
tiles[r, c].delEnt(1)
posList.append((r, c))
vals = list(zip(posList, vals))
return vals
def forward(self, ent, env):
s = torchlib.Stim(ent, env, self.config)
val = self.valNet(s.conv, s.flat, s.ents)
actions = ActionTree(env, ent, ActionV2).actions()
_, move, attk = actions
# Actions
moveArg, moveOuts = self.moveNet(env, ent, move, s)
attk, attkArg, attkOuts = self.attackNet(env, ent, attk, s)
action = (move, attk)
arguments = (moveArg, attkArg)
outs = (moveOuts, *attkOuts)
return action, arguments, outs, val
def prepareInput(self, ent, env):
s = torchlib.Stim(ent, env, self.config)
return s.flat.unsqueeze(0), s.ents.unsqueeze(0)
