def CFagentv2(defaults):
env = Game(**defaults)
mover = Mover(env, _extra_dim=1, **defaults)
teleporter = Teleporter(env, **defaults)
buffer = ReplayBuffer(**defaults)
CFagent = CFAgent(env, **defaults)
CFbuffer = CFReplayBuffer(**defaults)
simulator = Simulator(env, **defaults)
simbuffer = SimBuffer(**defaults)
collector = Collector(**defaults)
with Save(env, collector, mover, teleporter, CFagent, simulator, **defaults) as save:
intervention_idx, modified_board = teleporter.pre_process(env)
dones = CFagent.pre_process(env)
CF_dones, cfs = None, None
for frame in loop(env, collector, save, teleporter):
CFagent.counterfact2(env, dones, teleporter, simulator, CF_dones, cfs)
modified_board = teleporter.interveen(env.board, intervention_idx, modified_board)
actions = mover(modified_board)
observations, rewards, dones, info = env.step(actions)
modified_board, modified_rewards, modified_dones, teleport_rewards, intervention_idx = teleporter.modify(observations, rewards, dones, info)
buffer.teleporter_save_data(teleporter.boards, observations, teleporter.interventions, teleport_rewards, dones, intervention_idx)
mover.learn(modified_board, actions, modified_rewards, modified_dones)
board_before, board_after, intervention, tele_rewards, tele_dones = buffer.sample_data()
teleporter.learn(board_after, intervention, tele_rewards, tele_dones, board_before)
simbuffer.simulator_save_data(teleporter.boards, observations, teleporter.interventions, teleport_rewards, dones, intervention_idx)
board_before, board_after, intervention = simbuffer.sample_data()
lossboard = simulator.learn(board_before, board_after, intervention)
collector.collect_loss(lossboard)
collector.collect([rewards, modified_rewards, teleport_rewards], [dones, modified_dones])
CF_dones, cfs = CFagent.counterfact_check(dones, env, **defaults)
CFbuffer.CF_save_data(CFagent.boards, observations, CFagent.counterfactuals, rewards, dones, CF_dones)
CFboard, CFobs, cf, CFrewards, CFdones1 = CFbuffer.sample_data()
CFagent.learn(CFobs, cf, CFrewards, CFdones1, CFboard)
def graphTrain(defaults):
layers: List[LayerType] = environments[defaults['level']][2]
explorationN = defaults['K1']
data = Data(layers, defaults['graphMode'])
env = Game(**defaults)
mover = Mover(env, _extra_dim=1, **defaults)
teleporter = Teleporter(env, **defaults)
collector = Collector(**defaults)
model = BayesionNN(layers,
depth=defaults['depth'],
exploration=defaults['model_explore'],
samples=defaults['samples'])
use_model = defaults['use_model']
with Save(env, collector, mover, data, **defaults) as save:
convert = [env.layers.types.index(layer) for layer in layers]
player = env.layers.types.index(LayerType.Player)
goal = env.layers.types.index(LayerType.Goal)
old_states = [state for state in states(env.board, convert, layers)]
dones = tensor([0 for _ in range(env.batch)])
rewards = tensor([0 for _ in range(env.batch)])
eatCheese, interventions = ([True] * env.batch, [None] * env.batch)
for frame in loop(env, collector, save, teleporter=teleporter):
data.t = frame
new_states = [
state for state in states(env.board, convert, layers)
]
loss = transform(old_states,
new_states,
dones,
rewards,
data,
layers,
model,
use_model=use_model)
loss = transformNot(env.board,
new_states,
player,
goal,
convert,
data,
layers,
model,
use_model=use_model)
stateChanged = [
old != new for old, new in zip(old_states, new_states)
]
shouldInterviene = [
cond1 or cond2 for cond1, cond2 in zip(stateChanged, eatCheese)
]
exploration = max((explorationN - frame) / explorationN,
defaults['softmax_cap'])
if use_model:
interventions = [
(getInterventionsmodel(
state, env.layers.types, layers, model, env,
data.layers_not_in(state), frame) if should else old)
for state, should, old in zip(new_states, shouldInterviene,
interventions)
]
else:
interventions = [
(getInterventions(env, state, data, exploration)
if should else old) for state, should, old in zip(
new_states, shouldInterviene, interventions)
]
modification = env.board[tensor(interventions)].unsqueeze(1)
teleporter.interventions = tensor(
[m.flatten().argmax().item() for m in list(modification)],
device=device)
modified_board = cat((env.board, modification), dim=1)
actions = mover(modified_board)
observations, rewards, dones, info = env.step(actions)
modified_board, modified_rewards, modified_dones, _, _ = teleporter.modify(
observations, rewards, dones, info)
mover.learn(modified_board, actions, modified_rewards,
modified_dones)
playerPositions = [
(t := env.layers.dict[LayerType.Player].positions[i][0])[1] *
env.layers.width + t[0] for i in range(env.batch)
]
eatCheese = [
intervention == player_pos for intervention, player_pos in zip(
teleporter.interventions, playerPositions)
]
old_states = new_states
collector.collect([rewards, modified_rewards],
[dones, modified_dones])
collector.collect_loss(loss)
