def test_simple_q_loss_function(self):
"""Tests the Simple-Q loss function results on all frameworks."""
config = dqn.simple_q.SimpleQConfig().rollouts(num_rollout_workers=0)
# Use very simple net (layer0=10 nodes, q-layer=2 nodes (2 actions)).
config.training(model={
"fcnet_hiddens": [10],
"fcnet_activation": "linear",
})
for fw in framework_iterator(config):
# Generate Trainer and get its default Policy object.
trainer = dqn.SimpleQTrainer(config=config, env="CartPole-v0")
policy = trainer.get_policy()
# Batch of size=2.
input_ = SampleBatch({
SampleBatch.CUR_OBS:
np.random.random(size=(2, 4)),
SampleBatch.ACTIONS:
np.array([0, 1]),
SampleBatch.REWARDS:
np.array([0.4, -1.23]),
SampleBatch.DONES:
np.array([False, False]),
SampleBatch.NEXT_OBS:
np.random.random(size=(2, 4)),
SampleBatch.EPS_ID:
np.array([1234, 1234]),
SampleBatch.AGENT_INDEX:
np.array([0, 0]),
SampleBatch.ACTION_LOGP:
np.array([-0.1, -0.1]),
SampleBatch.ACTION_DIST_INPUTS:
np.array([[0.1, 0.2], [-0.1, -0.2]]),
SampleBatch.ACTION_PROB:
np.array([0.1, 0.2]),
"q_values":
np.array([[0.1, 0.2], [0.2, 0.1]]),
})
# Get model vars for computing expected model outs (q-vals).
# 0=layer-kernel; 1=layer-bias; 2=q-val-kernel; 3=q-val-bias
vars = policy.get_weights()
if isinstance(vars, dict):
vars = list(vars.values())
vars_t = policy.target_model.variables()
if fw == "tf":
vars_t = policy.get_session().run(vars_t)
# Q(s,a) outputs.
q_t = np.sum(
one_hot(input_[SampleBatch.ACTIONS], 2) * fc(
fc(
input_[SampleBatch.CUR_OBS],
vars[0 if fw != "torch" else 2],
vars[1 if fw != "torch" else 3],
framework=fw,
),
vars[2 if fw != "torch" else 0],
vars[3 if fw != "torch" else 1],
framework=fw,
),
1,
)
# max[a'](Qtarget(s',a')) outputs.
q_target_tp1 = np.max(
fc(
fc(
input_[SampleBatch.NEXT_OBS],
vars_t[0 if fw != "torch" else 2],
vars_t[1 if fw != "torch" else 3],
framework=fw,
),
vars_t[2 if fw != "torch" else 0],
vars_t[3 if fw != "torch" else 1],
framework=fw,
),
1,
)
# TD-errors (Bellman equation).
td_error = q_t - config.gamma * input_[
SampleBatch.REWARDS] + q_target_tp1
# Huber/Square loss on TD-error.
expected_loss = huber_loss(td_error).mean()
if fw == "torch":
input_ = policy._lazy_tensor_dict(input_)
# Get actual out and compare.
if fw == "tf":
out = policy.get_session().run(
policy._loss,
feed_dict=policy._get_loss_inputs_dict(input_,
shuffle=False),
)
else:
out = (loss_torch if fw == "torch" else loss_tf)(policy,
policy.model,
None, input_)
check(out, expected_loss, decimals=1)
def _ddpg_loss_helper(self, train_batch, weights, ks, fw, gamma,
huber_threshold, l2_reg, sess):
"""Emulates DDPG loss functions for tf and torch."""
model_out_t = train_batch[SampleBatch.CUR_OBS]
target_model_out_tp1 = train_batch[SampleBatch.NEXT_OBS]
# get_policy_output
policy_t = sigmoid(2.0 * fc(
relu(fc(model_out_t, weights[ks[1]], weights[ks[0]],
framework=fw)), weights[ks[5]], weights[ks[4]]))
# Get policy output for t+1 (target model).
policy_tp1 = sigmoid(2.0 * fc(
relu(
fc(target_model_out_tp1,
weights[ks[3]],
weights[ks[2]],
framework=fw)), weights[ks[7]], weights[ks[6]]))
# Assume no smooth target policy.
policy_tp1_smoothed = policy_tp1
# Q-values for the actually selected actions.
# get_q_values
q_t = fc(relu(
fc(np.concatenate([model_out_t, train_batch[SampleBatch.ACTIONS]],
-1),
weights[ks[9]],
weights[ks[8]],
framework=fw)),
weights[ks[11]],
weights[ks[10]],
framework=fw)
twin_q_t = fc(relu(
fc(np.concatenate([model_out_t, train_batch[SampleBatch.ACTIONS]],
-1),
weights[ks[13]],
weights[ks[12]],
framework=fw)),
weights[ks[15]],
weights[ks[14]],
framework=fw)
# Q-values for current policy in given current state.
# get_q_values
q_t_det_policy = fc(relu(
fc(np.concatenate([model_out_t, policy_t], -1),
weights[ks[9]],
weights[ks[8]],
framework=fw)),
weights[ks[11]],
weights[ks[10]],
framework=fw)
# Target q network evaluation.
# target_model.get_q_values
q_tp1 = fc(relu(
fc(np.concatenate([target_model_out_tp1, policy_tp1_smoothed], -1),
weights[ks[17]],
weights[ks[16]],
framework=fw)),
weights[ks[19]],
weights[ks[18]],
framework=fw)
twin_q_tp1 = fc(relu(
fc(np.concatenate([target_model_out_tp1, policy_tp1_smoothed], -1),
weights[ks[21]],
weights[ks[20]],
framework=fw)),
weights[ks[23]],
weights[ks[22]],
framework=fw)
q_t_selected = np.squeeze(q_t, axis=-1)
twin_q_t_selected = np.squeeze(twin_q_t, axis=-1)
q_tp1 = np.minimum(q_tp1, twin_q_tp1)
q_tp1_best = np.squeeze(q_tp1, axis=-1)
dones = train_batch[SampleBatch.DONES]
rewards = train_batch[SampleBatch.REWARDS]
if fw == "torch":
dones = dones.float().numpy()
rewards = rewards.numpy()
q_tp1_best_masked = (1.0 - dones) * q_tp1_best
q_t_selected_target = rewards + gamma * q_tp1_best_masked
td_error = q_t_selected - q_t_selected_target
twin_td_error = twin_q_t_selected - q_t_selected_target
td_error = td_error + twin_td_error
errors = huber_loss(td_error, huber_threshold) + \
huber_loss(twin_td_error, huber_threshold)
critic_loss = np.mean(errors)
actor_loss = -np.mean(q_t_det_policy)
# Add l2-regularization if required.
for name, var in weights.items():
if re.match("default_policy/actor_(hidden_0|out)/kernel", name):
actor_loss += (l2_reg * l2_loss(var))
elif re.match("default_policy/sequential(_1)?/\\w+/kernel", name):
critic_loss += (l2_reg * l2_loss(var))
return critic_loss, actor_loss, td_error
def _sac_loss_helper(self, train_batch, weights, ks, log_alpha, fw, gamma,
sess):
"""Emulates SAC loss functions for tf and torch."""
# ks:
# 0=log_alpha
# 1=target log-alpha (not used)
# 2=action hidden bias
# 3=action hidden kernel
# 4=action out bias
# 5=action out kernel
# 6=Q hidden bias
# 7=Q hidden kernel
# 8=Q out bias
# 9=Q out kernel
# 14=target Q hidden bias
# 15=target Q hidden kernel
# 16=target Q out bias
# 17=target Q out kernel
alpha = np.exp(log_alpha)
# cls = TorchSquashedGaussian if fw == "torch" else SquashedGaussian
cls = TorchDirichlet if fw == "torch" else Dirichlet
model_out_t = train_batch[SampleBatch.CUR_OBS]
model_out_tp1 = train_batch[SampleBatch.NEXT_OBS]
target_model_out_tp1 = train_batch[SampleBatch.NEXT_OBS]
# get_policy_output
action_dist_t = cls(
fc(
relu(
fc(model_out_t,
weights[ks[1]],
weights[ks[0]],
framework=fw)), weights[ks[9]], weights[ks[8]]), None)
policy_t = action_dist_t.deterministic_sample()
log_pis_t = action_dist_t.logp(policy_t)
if sess:
log_pis_t = sess.run(log_pis_t)
policy_t = sess.run(policy_t)
log_pis_t = np.expand_dims(log_pis_t, -1)
# Get policy output for t+1.
action_dist_tp1 = cls(
fc(
relu(
fc(model_out_tp1,
weights[ks[1]],
weights[ks[0]],
framework=fw)), weights[ks[9]], weights[ks[8]]), None)
policy_tp1 = action_dist_tp1.deterministic_sample()
log_pis_tp1 = action_dist_tp1.logp(policy_tp1)
if sess:
log_pis_tp1 = sess.run(log_pis_tp1)
policy_tp1 = sess.run(policy_tp1)
log_pis_tp1 = np.expand_dims(log_pis_tp1, -1)
# Q-values for the actually selected actions.
# get_q_values
q_t = fc(relu(
fc(np.concatenate([model_out_t, train_batch[SampleBatch.ACTIONS]],
-1),
weights[ks[3]],
weights[ks[2]],
framework=fw)),
weights[ks[11]],
weights[ks[10]],
framework=fw)
# Q-values for current policy in given current state.
# get_q_values
q_t_det_policy = fc(relu(
fc(np.concatenate([model_out_t, policy_t], -1),
weights[ks[3]],
weights[ks[2]],
framework=fw)),
weights[ks[11]],
weights[ks[10]],
framework=fw)
# Target q network evaluation.
# target_model.get_q_values
if fw == "tf":
q_tp1 = fc(relu(
fc(np.concatenate([target_model_out_tp1, policy_tp1], -1),
weights[ks[7]],
weights[ks[6]],
framework=fw)),
weights[ks[15]],
weights[ks[14]],
framework=fw)
else:
assert fw == "tfe"
q_tp1 = fc(relu(
fc(np.concatenate([target_model_out_tp1, policy_tp1], -1),
weights[ks[7]],
weights[ks[6]],
framework=fw)),
weights[ks[9]],
weights[ks[8]],
framework=fw)
q_t_selected = np.squeeze(q_t, axis=-1)
q_tp1 -= alpha * log_pis_tp1
q_tp1_best = np.squeeze(q_tp1, axis=-1)
dones = train_batch[SampleBatch.DONES]
rewards = train_batch[SampleBatch.REWARDS]
if fw == "torch":
dones = dones.float().numpy()
rewards = rewards.numpy()
q_tp1_best_masked = (1.0 - dones) * q_tp1_best
q_t_selected_target = rewards + gamma * q_tp1_best_masked
base_td_error = np.abs(q_t_selected - q_t_selected_target)
td_error = base_td_error
critic_loss = [
np.mean(train_batch["weights"] *
huber_loss(q_t_selected_target - q_t_selected))
]
target_entropy = -np.prod((1, ))
alpha_loss = -np.mean(log_alpha * (log_pis_t + target_entropy))
actor_loss = np.mean(alpha * log_pis_t - q_t_det_policy)
return critic_loss, actor_loss, alpha_loss, td_error
def test_simple_q_loss_function(self):
"""Tests the Simple-Q loss function results on all frameworks."""
config = dqn.SIMPLE_Q_DEFAULT_CONFIG.copy()
# Run locally.
config["num_workers"] = 0
# Use very simple net (layer0=10 nodes, q-layer=2 nodes (2 actions)).
config["model"]["fcnet_hiddens"] = [10]
config["model"]["fcnet_activation"] = "linear"
for fw in framework_iterator(config):
# Generate Trainer and get its default Policy object.
trainer = dqn.SimpleQTrainer(config=config, env="CartPole-v0")
policy = trainer.get_policy()
# Batch of size=2.
input_ = {
SampleBatch.CUR_OBS: np.random.random(size=(2, 4)),
SampleBatch.ACTIONS: np.array([0, 1]),
SampleBatch.REWARDS: np.array([0.4, -1.23]),
SampleBatch.DONES: np.array([False, False]),
SampleBatch.NEXT_OBS: np.random.random(size=(2, 4))
}
# Get model vars for computing expected model outs (q-vals).
# 0=layer-kernel; 1=layer-bias; 2=q-val-kernel; 3=q-val-bias
vars = policy.get_weights()
if isinstance(vars, dict):
vars = list(vars.values())
vars_t = policy.target_q_func_vars
if fw == "tf":
vars_t = policy.get_session().run(vars_t)
# Q(s,a) outputs.
q_t = np.sum(
one_hot(input_[SampleBatch.ACTIONS], 2) *
fc(fc(input_[SampleBatch.CUR_OBS],
vars[0 if fw != "torch" else 2],
vars[1 if fw != "torch" else 3],
framework=fw),
vars[2 if fw != "torch" else 0],
vars[3 if fw != "torch" else 1],
framework=fw), 1)
# max[a'](Qtarget(s',a')) outputs.
q_target_tp1 = np.max(
fc(fc(input_[SampleBatch.NEXT_OBS],
vars_t[0 if fw != "torch" else 2],
vars_t[1 if fw != "torch" else 3],
framework=fw),
vars_t[2 if fw != "torch" else 0],
vars_t[3 if fw != "torch" else 1],
framework=fw), 1)
# TD-errors (Bellman equation).
td_error = q_t - config["gamma"] * input_[SampleBatch.REWARDS] + \
q_target_tp1
# Huber/Square loss on TD-error.
expected_loss = huber_loss(td_error).mean()
if fw == "torch":
input_ = policy._lazy_tensor_dict(input_)
# Get actual out and compare.
if fw == "tf":
out = policy.get_session().run(
policy._loss,
feed_dict=policy._get_loss_inputs_dict(input_,
shuffle=False))
else:
out = (loss_torch if fw == "torch" else loss_tf)(policy,
policy.model,
None, input_)
check(out, expected_loss, decimals=1)
