class TRPOAgent:
TRAJECTORY_SIZE = 1024
VF_BATCHSIZE = 64
MAX_KL = 0.01
GAMMA = 0.99
GAE_LAMBDA = 0.98
ENV_ID = "Pendulum-v0"
OBS_SPACE = 3
ACTION_SPACE = 1
def __init__(self):
self.policy = PolicyNetwork(action_space=self.ACTION_SPACE)
self.value_network = ValueNetwork()
self.env = gym.make(self.ENV_ID)
self.global_steps = 0
self.history = []
self.hiscore = None
def play(self, n_iters):
self.epi_reward = 0
self.epi_steps = 0
self.state = self.env.reset()
for _ in range(n_iters):
trajectory = self.generate_trajectory()
trajectory = self.compute_advantage(trajectory)
self.update_policy(trajectory)
self.update_vf(trajectory)
return self.history
def generate_trajectory(self):
"""generate trajectory on current policy
"""
trajectory = {
"s":
np.zeros((self.TRAJECTORY_SIZE, self.OBS_SPACE), dtype=np.float32),
"a":
np.zeros((self.TRAJECTORY_SIZE, self.ACTION_SPACE),
dtype=np.float32),
"r":
np.zeros((self.TRAJECTORY_SIZE, 1), dtype=np.float32),
"s2":
np.zeros((self.TRAJECTORY_SIZE, self.OBS_SPACE), dtype=np.float32),
"done":
np.zeros((self.TRAJECTORY_SIZE, 1), dtype=np.float32)
}
state = self.state
for i in range(self.TRAJECTORY_SIZE):
action = self.policy.sample_action(state)
next_state, reward, done, _ = self.env.step(action)
trajectory["s"][i] = state
trajectory["a"][i] = action
trajectory["r"][i] = reward
trajectory["s2"][i] = next_state
trajectory["done"][i] = done
self.epi_reward += reward
self.epi_steps += 1
self.global_steps += 1
if done:
state = self.env.reset()
self.history.append(self.epi_reward)
recent_score = sum(self.history[-10:]) / 10
print("====" * 5)
print("Episode:", len(self.history))
print("Episode reward:", self.epi_reward)
print("Global steps:", self.global_steps)
if len(self.history) > 100 and (self.hiscore is None or
recent_score > self.hiscore):
print("*HISCORE UPDATED:", recent_score)
self.save_model()
self.hiscore = recent_score
self.epi_reward = 0
self.epi_steps = 0
else:
state = next_state
self.state = state
return trajectory
def compute_advantage(self, trajectory):
"""Compute
Args:
trajectory ([type]): [description]
"""
trajectory["vpred"] = self.value_network(trajectory["s"]).numpy()
trajectory["vpred_next"] = self.value_network(trajectory["s2"]).numpy()
is_nonterminals = 1 - trajectory["done"]
deltas = trajectory["r"] + self.GAMMA * is_nonterminals * trajectory[
"vpred_next"] - trajectory["vpred"]
advantages = np.zeros_like(deltas, dtype=np.float32)
lastgae = 0
for i in reversed(range(len(deltas))):
lastgae = deltas[
i] + self.GAMMA * self.GAE_LAMBDA * is_nonterminals[i] * lastgae
advantages[i] = lastgae
trajectory["adv"] = (advantages -
advantages.mean()) / (advantages.std() + 1e-8)
#trajectory["adv"] = advantages
trajectory["vftarget"] = trajectory["adv"] + trajectory["vpred"]
return trajectory
def update_policy(self, trajectory):
def flattengrads(grads):
flatgrads_list = [
tf.reshape(grad, shape=[1, -1]) for grad in grads
]
flatgrads = tf.concat(flatgrads_list, axis=1)
return flatgrads
actions = tf.convert_to_tensor(trajectory["a"], dtype=tf.float32)
states = tf.convert_to_tensor(trajectory["s"], dtype=tf.float32)
advantages = tf.convert_to_tensor(trajectory["adv"], dtype=tf.float32)
old_means, old_stdevs = self.policy(states)
old_logp = compute_logprob(old_means, old_stdevs, actions)
with tf.GradientTape() as tape:
new_means, new_stdevs = self.policy(states)
new_logp = compute_logprob(new_means, new_stdevs, actions)
loss = tf.exp(new_logp - old_logp) * advantages
loss = tf.reduce_mean(loss)
g = tape.gradient(loss, self.policy.trainable_variables)
g = tf.transpose(flattengrads(g))
@tf.function
def hvp_func(vector):
"""Compute hessian-vector product
"""
with tf.GradientTape() as t2:
with tf.GradientTape() as t1:
new_means, new_stdevs = self.policy(states)
kl = compute_kl(old_means, old_stdevs, new_means,
new_stdevs)
meankl = tf.reduce_mean(kl)
kl_grads = t1.gradient(meankl, self.policy.trainable_variables)
kl_grads = flattengrads(kl_grads)
grads_vector_product = tf.matmul(kl_grads, vector)
hvp = t2.gradient(grads_vector_product,
self.policy.trainable_variables)
hvp = tf.transpose(flattengrads(hvp))
return hvp + vector * 1e-2 #: 共役勾配法の安定化のために微小量を加える
step_direction = cg(hvp_func, g)
shs = tf.matmul(tf.transpose(step_direction), hvp_func(step_direction))
lm = tf.sqrt(2 * self.MAX_KL / shs)
fullstep = lm * step_direction
expected_improve = tf.matmul(tf.transpose(g), fullstep)
fullstep = restore_shape(fullstep, self.policy.trainable_variables)
params_old = [var.numpy() for var in self.policy.trainable_variables]
old_loss = loss
for stepsize in [0.5**i for i in range(10)]:
params_new = [
p + step * stepsize for p, step in zip(params_old, fullstep)
]
self.policy.set_weights(params_new)
new_means, new_stdevs = self.policy(states)
new_logp = compute_logprob(new_means, new_stdevs, actions)
new_loss = tf.reduce_mean(tf.exp(new_logp - old_logp) * advantages)
improve = new_loss - old_loss
kl = compute_kl(old_means, old_stdevs, new_means, new_stdevs)
mean_kl = tf.reduce_mean(kl)
print(f"Expected: {expected_improve} Actual: {improve}")
print(f"KL {mean_kl}")
if mean_kl > self.MAX_KL * 1.5:
print("violated KL constraint. shrinking step.")
elif improve < 0:
print("surrogate didn't improve. shrinking step.")
else:
print("Stepsize OK!")
break
else:
print("更新に失敗")
self.policy.set_weights(params_old)
def update_vf(self, trajectory):
for _ in range(self.TRAJECTORY_SIZE // self.VF_BATCHSIZE):
indx = np.random.choice(self.TRAJECTORY_SIZE,
self.VF_BATCHSIZE,
replace=True)
with tf.GradientTape() as tape:
vpred = self.value_network(trajectory["s"][indx])
vtarget = trajectory["vftarget"][indx]
loss = tf.reduce_mean(tf.square(vtarget - vpred))
variables = self.value_network.trainable_variables
grads = tape.gradient(loss, variables)
self.value_network.optimizer.apply_gradients(zip(grads, variables))
def save_model(self):
self.policy.save_weights("checkpoints/actor")
self.value_network.save_weights("checkpoints/critic")
print()
print("Model Saved")
print()
def load_model(self):
self.policy.load_weights("checkpoints/actor")
self.value_network.load_weights("checkpoints/critic")
def test_play(self, n, monitordir, load_model=False):
if load_model:
self.load_model()
if monitordir:
env = wrappers.Monitor(gym.make(self.ENV_ID),
monitordir,
force=True,
video_callable=(lambda ep: ep % 1 == 0))
else:
env = gym.make(self.ENV_ID)
for i in range(n):
total_reward = 0
steps = 0
done = False
state = env.reset()
while not done:
action = self.policy.sample_action(state)
next_state, reward, done, _ = env.step(action)
state = next_state
total_reward += reward
steps += 1
print()
print(f"Test Play {i}: {total_reward}")
print(f"Steps:", steps)
print()
class PPOAgent:
GAMMA = 0.99
GAE_LAMBDA = 0.95
CLIPRANGE = 0.2
OPT_ITER = 20
BATCH_SIZE = 2048
def __init__(self,
env_id,
action_space,
trajectory_size=256,
n_envs=1,
max_timesteps=1500):
self.env_id = env_id
self.n_envs = n_envs
self.trajectory_size = trajectory_size
self.vecenv = VecEnv(env_id=self.env_id,
n_envs=self.n_envs,
max_timesteps=max_timesteps)
self.policy = PolicyNetwork(action_space=action_space)
self.old_policy = PolicyNetwork(action_space=action_space)
self.critic = CriticNetwork()
self.r_running_stats = util.RunningStats(shape=(action_space, ))
self._init_network()
def _init_network(self):
env = gym.make(self.env_id)
state = np.atleast_2d(env.reset())
self.policy(state)
self.old_policy(state)
def run(self, n_updates, logdir):
self.summary_writer = tf.summary.create_file_writer(str(logdir))
history = {"steps": [], "scores": []}
states = self.vecenv.reset()
hiscore = None
for epoch in range(n_updates):
for _ in range(self.trajectory_size):
actions = self.policy.sample_action(states)
next_states = self.vecenv.step(actions)
states = next_states
trajectories = self.vecenv.get_trajectories()
for trajectory in trajectories:
self.r_running_stats.update(trajectory["r"])
trajectories = self.compute_advantage(trajectories)
states, actions, advantages, vtargs = self.create_minibatch(
trajectories)
vloss = self.update_critic(states, vtargs)
self.update_policy(states, actions, advantages)
global_steps = (epoch + 1) * self.trajectory_size * self.n_envs
train_scores = np.array([traj["r"].sum() for traj in trajectories])
if epoch % 1 == 0:
test_scores, total_steps = self.play(n=1)
test_scores, total_steps = np.array(test_scores), np.array(
total_steps)
history["steps"].append(global_steps)
history["scores"].append(test_scores.mean())
ma_score = sum(history["scores"][-10:]) / 10
with self.summary_writer.as_default():
tf.summary.scalar("test_score",
test_scores.mean(),
step=epoch)
tf.summary.scalar("test_steps",
total_steps.mean(),
step=epoch)
print(
f"Epoch {epoch}, {global_steps//1000}K, {test_scores.mean()}"
)
if epoch // 10 > 10 and (hiscore is None or ma_score > hiscore):
self.save_model()
hiscore = ma_score
print("Model Saved")
with self.summary_writer.as_default():
tf.summary.scalar("value_loss", vloss, step=epoch)
tf.summary.scalar("train_score",
train_scores.mean(),
step=epoch)
return history
def compute_advantage(self, trajectories):
"""
Generalized Advantage Estimation (GAE, 2016)
"""
for trajectory in trajectories:
trajectory["v_pred"] = self.critic(trajectory["s"]).numpy()
trajectory["v_pred_next"] = self.critic(trajectory["s2"]).numpy()
is_nonterminals = 1 - trajectory["done"]
normed_rewards = (trajectory["r"] /
(np.sqrt(self.r_running_stats.var) + 1e-4))
deltas = normed_rewards + self.GAMMA * is_nonterminals * trajectory[
"v_pred_next"] - trajectory["v_pred"]
advantages = np.zeros_like(deltas, dtype=np.float32)
lastgae = 0
for i in reversed(range(len(deltas))):
lastgae = deltas[
i] + self.GAMMA * self.GAE_LAMBDA * is_nonterminals[
i] * lastgae
advantages[i] = lastgae
trajectory["advantage"] = advantages
trajectory["R"] = advantages + trajectory["v_pred"]
return trajectories
def update_policy(self, states, actions, advantages):
self.old_policy.set_weights(self.policy.get_weights())
indices = np.random.choice(range(states.shape[0]),
(self.OPT_ITER, self.BATCH_SIZE))
for i in range(self.OPT_ITER):
idx = indices[i]
old_means, old_stdevs = self.old_policy(states[idx])
old_logprob = self.compute_logprob(old_means, old_stdevs,
actions[idx])
with tf.GradientTape() as tape:
new_means, new_stdevs = self.policy(states[idx])
new_logprob = self.compute_logprob(new_means, new_stdevs,
actions[idx])
ratio = tf.exp(new_logprob - old_logprob)
ratio_clipped = tf.clip_by_value(ratio, 1 - self.CLIPRANGE,
1 + self.CLIPRANGE)
loss_unclipped = ratio * advantages[idx]
loss_clipped = ratio_clipped * advantages[idx]
loss = tf.minimum(loss_unclipped, loss_clipped)
loss = -1 * tf.reduce_mean(loss)
grads = tape.gradient(loss, self.policy.trainable_variables)
grads, _ = tf.clip_by_global_norm(grads, 0.5)
self.policy.optimizer.apply_gradients(
zip(grads, self.policy.trainable_variables))
def update_critic(self, states, v_targs):
losses = []
indices = np.random.choice(range(states.shape[0]),
(self.OPT_ITER, self.BATCH_SIZE))
for i in range(self.OPT_ITER):
idx = indices[i]
old_vpred = self.critic(states[idx])
with tf.GradientTape() as tape:
vpred = self.critic(states[idx])
vpred_clipped = old_vpred + tf.clip_by_value(
vpred - old_vpred, -self.CLIPRANGE, self.CLIPRANGE)
loss = tf.maximum(tf.square(v_targs[idx] - vpred),
tf.square(v_targs[idx] - vpred_clipped))
loss = tf.reduce_mean(loss)
grads = tape.gradient(loss, self.critic.trainable_variables)
grads, _ = tf.clip_by_global_norm(grads, 0.5)
self.critic.optimizer.apply_gradients(
zip(grads, self.critic.trainable_variables))
losses.append(loss)
return np.array(losses).mean()
@tf.function
def compute_logprob(self, means, stdevs, actions):
"""ガウス分布の確率密度関数よりlogp(x)を計算
logp(x) = -0.5 log(2π) - log(std) -0.5 * ((x - mean) / std )^2
"""
logprob = -0.5 * np.log(2 * np.pi)
logprob += -tf.math.log(stdevs)
logprob += -0.5 * tf.square((actions - means) / stdevs)
logprob = tf.reduce_sum(logprob, axis=1, keepdims=True)
return logprob
def create_minibatch(self, trajectories):
states = np.vstack([traj["s"] for traj in trajectories])
actions = np.vstack([traj["a"] for traj in trajectories])
advantages = np.vstack([traj["advantage"] for traj in trajectories])
v_targs = np.vstack([traj["R"] for traj in trajectories])
return states, actions, advantages, v_targs
def save_model(self):
self.policy.save_weights("checkpoints/policy")
self.critic.save_weights("checkpoints/critic")
def load_model(self):
self.policy.load_weights("checkpoints/policy")
self.critic.load_weights("checkpoints/critic")
def play(self, n=1, monitordir=None, verbose=False):
if monitordir:
env = wrappers.Monitor(gym.make(self.env_id),
monitordir,
force=True,
video_callable=(lambda ep: True))
else:
env = gym.make(self.env_id)
total_rewards = []
total_steps = []
for _ in range(n):
state = env.reset()
done = False
total_reward = 0
steps = 0
while not done:
steps += 1
action = self.policy.sample_action(state)
next_state, reward, done, _ = env.step(action[0])
if verbose:
mean, sd = self.policy(np.atleast_2d(state))
print(mean, sd)
print(reward)
total_reward += reward
if done:
break
else:
state = next_state
total_rewards.append(total_reward)
total_steps.append(steps)
print()
print(total_reward, steps)
print()
return total_rewards, total_steps
