def log_strategy(writer: SummaryWriter, policy: NnPolicyWrapper,
global_step: int):
infoset = KuhnInfoset(0, ())
for card in range(3):
infoset.card = card
infoset.bet_sequence = ()
aggressive_action_prob = policy.aggressive_action_prob(infoset)
node_name = "strategy/%s/p0_open" % card_to_str(card)
writer.add_scalar(node_name,
aggressive_action_prob,
global_step=global_step)
infoset.bet_sequence = (0, )
aggressive_action_prob = policy.aggressive_action_prob(infoset)
node_name = "strategy/%s/p0_check/p1" % card_to_str(card)
writer.add_scalar(node_name,
aggressive_action_prob,
global_step=global_step)
infoset.bet_sequence = (0, 1)
aggressive_action_prob = policy.aggressive_action_prob(infoset)
node_name = "strategy/%s/p0_check/p1_bet/p0" % card_to_str(card)
writer.add_scalar(node_name,
aggressive_action_prob,
global_step=global_step)
infoset.bet_sequence = (1, )
aggressive_action_prob = policy.aggressive_action_prob(infoset)
node_name = "strategy/%s/p0_bet/p1" % card_to_str(card)
writer.add_scalar(node_name,
aggressive_action_prob,
global_step=global_step)
def log_qvals(writer: SummaryWriter, policy: QPolicy, global_step: int):
infoset = KuhnInfoset(0, ())
for card in range(3):
infoset.card = card
infoset.bet_sequence = ()
state = torch.from_numpy(
infoset_to_state(infoset)).float().unsqueeze(0).to(device)
q_vals = policy.qnetwork_local.forward(state).cpu().numpy()[0]
node_name = "q_vals/%s/p0_open" % card_to_str(card)
writer.add_scalar(node_name,
q_vals[1] - q_vals[0],
global_step=global_step)
infoset.bet_sequence = (0, )
state = torch.from_numpy(
infoset_to_state(infoset)).float().unsqueeze(0).to(device)
q_vals = policy.qnetwork_local.forward(state).cpu().numpy()[0]
node_name = "q_vals/%s/p0_check/p1" % card_to_str(card)
writer.add_scalar(node_name,
q_vals[1] - q_vals[0],
global_step=global_step)
infoset.bet_sequence = (0, 1)
state = torch.from_numpy(
infoset_to_state(infoset)).float().unsqueeze(0).to(device)
q_vals = policy.qnetwork_local.forward(state).cpu().numpy()[0]
node_name = "q_vals/%s/p0_check/p1_bet/p0" % card_to_str(card)
writer.add_scalar(node_name,
q_vals[1] - q_vals[0],
global_step=global_step)
infoset.bet_sequence = (1, )
state = torch.from_numpy(
infoset_to_state(infoset)).float().unsqueeze(0).to(device)
q_vals = policy.qnetwork_local.forward(state).cpu().numpy()[0]
node_name = "q_vals/%s/p0_bet/p1" % card_to_str(card)
writer.add_scalar(node_name,
q_vals[1] - q_vals[0],
global_step=global_step)