def process_batch(engine_for_batch, batch):
optimizer.zero_grad()
loss_v = utils.calc_loss_dqn(batch, net, target_net.target_model, gamma=params.gamma, device=device)
loss_v.backward()
optimizer.step()
epsilon_tracker.frame(engine_for_batch.state.iteration)
if engine_for_batch.state.iteration % params.target_net_sync == 0:
target_net.sync()
if engine.state.iteration % EVAL_EVERY_FRAME == 0:
eval_states = getattr(engine.state, "eval_states", None)
if eval_states is None:
eval_states = buffer.sample(STATES_TO_EVALUATE)
eval_states = [np.array(transition.state, copy=False) for transition in eval_states]
eval_states = np.array(eval_states, copy=False)
engine.state.eval_states = eval_states
evaluate_states(eval_states, net, device, engine)
return {
"loss": loss_v.item(),
"epsilon": selector.epsilon,
}
def process_batch(engine_for_batch, batch_data):
batch, batch_indices, batch_weights = batch_data
optimizer.zero_grad()
loss_v, sample_priority = calc_loss(batch,
batch_weights,
net,
target_net.target_model,
gamma=params.gamma,
_device=str(device))
loss_v.backward()
optimizer.step()
buffer.update_priorities(batch_indices, sample_priority)
epsilon_tracker.frame(engine_for_batch.state.iteration)
if engine_for_batch.state.iteration % params.target_net_sync == 0:
target_net.sync()
return {
"loss": loss_v.item(),
"epsilon": selector.epsilon,
"beta": buffer.update_beta(engine.state.iteration),
}
def process_batch(engine_for_batch, batch):
optimizer.zero_grad()
loss_v = utils.calc_loss_dqn(batch, net, target_net.target_model, gamma=params.gamma, device=device)
loss_v.backward()
optimizer.step()
epsilon_tracker.frame(engine_for_batch.state.iteration)
if engine_for_batch.state.iteration % params.target_net_sync == 0:
target_net.sync()
return {
"loss": loss_v.item(),
"epsilon": selector.epsilon,
}