def ppo_update(episodes, policy, optimizer, baseline, prms):
# Get values to device
states, actions, rewards, dones, next_states = get_episode_values(episodes)
# Update value function & Compute advantages
returns = ch.td.discount(prms['gamma'], rewards, dones)
advantages = compute_advantages(baseline, prms['tau'], prms['gamma'], rewards, dones, states, next_states)
advantages = ch.normalize(advantages, epsilon=1e-8).detach()
# Calculate loss between states and action in the network
with torch.no_grad():
old_log_probs = policy.log_prob(states, actions)
# Initialize inner loop PPO optimizer
av_loss = 0.0
for ppo_epoch in range(prms['ppo_epochs']):
new_log_probs = policy.log_prob(states, actions)
# Compute the policy loss
policy_loss = ppo.policy_loss(new_log_probs, old_log_probs, advantages, clip=prms['ppo_clip_ratio'])
# Adapt model based on the loss
optimizer.zero_grad()
policy_loss.backward()
optimizer.step()
baseline.fit(states, returns)
av_loss += policy_loss.item()
return av_loss / prms['ppo_epochs']
def update(replay, optimizer, policy, env, lr_schedule):
_, next_state_value = policy(replay[-1].next_state())
advantages = pg.generalized_advantage(GAMMA, TAU, replay.reward(),
replay.done(), replay.value(),
next_state_value)
advantages = ch.utils.normalize(advantages, epsilon=1e-5).view(-1, 1)
rewards = [a + v for a, v in zip(advantages, replay.value())]
for i, sars in enumerate(replay):
sars.reward = rewards[i].detach()
sars.advantage = advantages[i].detach()
# Logging
policy_losses = []
entropies = []
value_losses = []
mean = lambda a: sum(a) / len(a)
# Perform some optimization steps
for step in range(PPO_EPOCHS * PPO_NUM_BATCHES):
batch = replay.sample(PPO_BSZ)
masses, values = policy(batch.state())
# Compute losses
new_log_probs = masses.log_prob(batch.action()).sum(-1, keepdim=True)
entropy = masses.entropy().sum(-1).mean()
policy_loss = ppo.policy_loss(new_log_probs,
batch.log_prob(),
batch.advantage(),
clip=PPO_CLIP)
value_loss = ppo.state_value_loss(values,
batch.value().detach(),
batch.reward(),
clip=PPO_CLIP)
loss = policy_loss - ENT_WEIGHT * entropy + V_WEIGHT * value_loss
# Take optimization step
optimizer.zero_grad()
loss.backward()
th.nn.utils.clip_grad_norm_(policy.parameters(), GRAD_NORM)
optimizer.step()
policy_losses.append(policy_loss)
entropies.append(entropy)
value_losses.append(value_loss)
# Log metrics
if dist.get_rank() == 0:
env.log('policy loss', mean(policy_losses).item())
env.log('policy entropy', mean(entropies).item())
env.log('value loss', mean(value_losses).item())
ppt.plot(mean(env.all_rewards[-10000:]), 'PPO results')
# Update the parameters on schedule
if LINEAR_SCHEDULE:
lr_schedule.step()
def test_ppo_policy_loss(self):
for _ in range(10):
for shape in [(1, BSZ), (BSZ, 1), (BSZ, )]:
for clip in [0.0, 0.1, 0.2, 1.0]:
new_log_probs = th.randn(BSZ)
old_log_probs = th.randn(BSZ)
advantages = th.randn(BSZ)
ref = ref_policy_loss(new_log_probs,
old_log_probs,
advantages,
clip=clip)
loss = ppo.policy_loss(new_log_probs.view(*shape),
old_log_probs.view(*shape),
advantages.view(*shape),
clip=clip)
self.assertAlmostEqual(loss.item(), ref.item())
def single_ppo_update(episodes, learner, baseline, params, anil=False):
# Get values to device
states, actions, rewards, dones, next_states = get_episode_values(episodes)
# Update value function & Compute advantages
advantages = compute_advantages(baseline, params['tau'], params['gamma'],
rewards, dones, states, next_states)
advantages = ch.normalize(advantages, epsilon=1e-8).detach()
# Calculate loss between states and action in the network
with torch.no_grad():
old_log_probs = learner.log_prob(states, actions)
# Initialize inner loop PPO optimizer
new_log_probs = learner.log_prob(states, actions)
# Compute the policy loss
loss = ppo.policy_loss(new_log_probs,
old_log_probs,
advantages,
clip=params['ppo_clip_ratio'])
# Adapt model based on the loss
learner.adapt(loss, allow_unused=anil)
return loss
def fast_adapt_ppo(task, learner, baseline, params, anil=False, render=False):
# During inner loop adaptation we do not store gradients for the network body
if anil:
learner.module.turn_off_body_grads()
for step in range(params['adapt_steps']):
# Collect adaptation / support episodes
support_episodes = task.run(learner,
episodes=params['adapt_batch_size'],
render=render)
# Get values to device
states, actions, rewards, dones, next_states = get_episode_values(
support_episodes)
# Update value function & Compute advantages
advantages = compute_advantages(baseline, params['tau'],
params['gamma'], rewards, dones,
states, next_states)
advantages = ch.normalize(advantages, epsilon=1e-8).detach()
# Calculate loss between states and action in the network
with torch.no_grad():
old_log_probs = learner.log_prob(states, actions)
# Initialize inner loop PPO optimizer
av_loss = 0.0
for ppo_epoch in range(params['ppo_epochs']):
new_log_probs = learner.log_prob(states, actions)
# Compute the policy loss
loss = ppo.policy_loss(new_log_probs,
old_log_probs,
advantages,
clip=params['ppo_clip_ratio'])
# Adapt model based on the loss
learner.adapt(loss, allow_unused=anil)
av_loss += loss
# We need to include the body network parameters for the query set
if anil:
learner.module.turn_on_body_grads()
# Collect evaluation / query episodes
query_episodes = task.run(learner, episodes=params['adapt_batch_size'])
# Get values to device
states, actions, rewards, dones, next_states = get_episode_values(
query_episodes)
# Update value function & Compute advantages
advantages = compute_advantages(baseline, params['tau'], params['gamma'],
rewards, dones, states, next_states)
advantages = ch.normalize(advantages, epsilon=1e-8).detach()
# Calculate loss between states and action in the network
with torch.no_grad():
old_log_probs = learner.log_prob(states, actions)
new_log_probs = learner.log_prob(states, actions)
# Compute the policy loss
valid_loss = ppo.policy_loss(new_log_probs,
old_log_probs,
advantages,
clip=params['ppo_clip_ratio'])
# Calculate the average reward of the evaluation episodes
query_rew = query_episodes.reward().sum().item(
) / params['adapt_batch_size']
query_success_rate = get_ep_successes(
query_episodes, params['max_path_length']) / params['adapt_batch_size']
return valid_loss, query_rew, query_success_rate
def main(
env_name='AntDirection-v1',
adapt_lr=0.1,
meta_lr=3e-4,
adapt_steps=3,
num_iterations=1000,
meta_bsz=40,
adapt_bsz=20,
ppo_clip=0.3,
ppo_steps=5,
tau=1.00,
gamma=0.99,
eta=0.0005,
adaptive_penalty=False,
kl_target=0.01,
num_workers=4,
seed=421,
):
random.seed(seed)
np.random.seed(seed)
th.manual_seed(seed)
def make_env():
env = gym.make(env_name)
env = ch.envs.ActionSpaceScaler(env)
return env
env = l2l.gym.AsyncVectorEnv([make_env for _ in range(num_workers)])
env.seed(seed)
env = ch.envs.ActionSpaceScaler(env)
env = ch.envs.Torch(env)
policy = DiagNormalPolicy(input_size=env.state_size,
output_size=env.action_size,
hiddens=[64, 64],
activation='tanh')
meta_learner = l2l.algorithms.MAML(policy, lr=meta_lr)
baseline = LinearValue(env.state_size, env.action_size)
opt = optim.Adam(meta_learner.parameters(), lr=meta_lr)
for iteration in range(num_iterations):
iteration_reward = 0.0
iteration_replays = []
iteration_policies = []
# Sample Trajectories
for task_config in tqdm(env.sample_tasks(meta_bsz),
leave=False,
desc='Data'):
clone = deepcopy(meta_learner)
env.set_task(task_config)
env.reset()
task = ch.envs.Runner(env)
task_replay = []
task_policies = []
# Fast Adapt
for step in range(adapt_steps):
for p in clone.parameters():
p.detach_().requires_grad_()
task_policies.append(deepcopy(clone))
train_episodes = task.run(clone, episodes=adapt_bsz)
clone = fast_adapt_a2c(clone,
train_episodes,
adapt_lr,
baseline,
gamma,
tau,
first_order=True)
task_replay.append(train_episodes)
# Compute Validation Loss
for p in clone.parameters():
p.detach_().requires_grad_()
task_policies.append(deepcopy(clone))
valid_episodes = task.run(clone, episodes=adapt_bsz)
task_replay.append(valid_episodes)
iteration_reward += valid_episodes.reward().sum().item(
) / adapt_bsz
iteration_replays.append(task_replay)
iteration_policies.append(task_policies)
# Print statistics
print('\nIteration', iteration)
adaptation_reward = iteration_reward / meta_bsz
print('adaptation_reward', adaptation_reward)
# ProMP meta-optimization
for ppo_step in tqdm(range(ppo_steps), leave=False, desc='Optim'):
promp_loss = 0.0
kl_total = 0.0
for task_replays, old_policies in zip(iteration_replays,
iteration_policies):
new_policy = meta_learner.clone()
states = task_replays[0].state()
actions = task_replays[0].action()
rewards = task_replays[0].reward()
dones = task_replays[0].done()
next_states = task_replays[0].next_state()
old_policy = old_policies[0]
(old_density, new_density, old_log_probs,
new_log_probs) = precompute_quantities(
states, actions, old_policy, new_policy)
advantages = compute_advantages(baseline, tau, gamma, rewards,
dones, states, next_states)
advantages = ch.normalize(advantages).detach()
for step in range(adapt_steps):
# Compute KL penalty
kl_pen = kl_divergence(old_density, new_density).mean()
kl_total += kl_pen.item()
# Update the clone
surr_loss = trpo.policy_loss(new_log_probs, old_log_probs,
advantages)
new_policy.adapt(surr_loss)
# Move to next adaptation step
states = task_replays[step + 1].state()
actions = task_replays[step + 1].action()
rewards = task_replays[step + 1].reward()
dones = task_replays[step + 1].done()
next_states = task_replays[step + 1].next_state()
old_policy = old_policies[step + 1]
(old_density, new_density, old_log_probs,
new_log_probs) = precompute_quantities(
states, actions, old_policy, new_policy)
# Compute clip loss
advantages = compute_advantages(baseline, tau, gamma,
rewards, dones, states,
next_states)
advantages = ch.normalize(advantages).detach()
clip_loss = ppo.policy_loss(new_log_probs,
old_log_probs,
advantages,
clip=ppo_clip)
# Combine into ProMP loss
promp_loss += clip_loss + eta * kl_pen
kl_total /= meta_bsz * adapt_steps
promp_loss /= meta_bsz * adapt_steps
opt.zero_grad()
promp_loss.backward(retain_graph=True)
opt.step()
# Adapt KL penalty based on desired target
if adaptive_penalty:
if kl_total < kl_target / 1.5:
eta /= 2.0
elif kl_total > kl_target * 1.5:
eta *= 2.0
def update(replay, optimizer, policy, env, lr_schedule):
_, next_state_value = policy(replay[-1].next_state)
# NOTE: Kostrikov uses GAE here.
advantages = ch.generalized_advantage(GAMMA, TAU, replay.reward(),
replay.done(), replay.value(),
next_state_value)
advantages = advantages.view(-1, 1)
# advantages = ch.utils.normalize(advantages, epsilon=1e-5).view(-1, 1)
# rewards = [a + v for a, v in zip(advantages, replay.value())]
rewards = advantages + replay.value()
# rewards = ch.discount(GAMMA,
# replay.reward(),
# replay.done(),
# bootstrap=next_state_value)
# rewards = rewards.detach()
# advantages = rewards.detach() - replay.value().detach()
# advantages = ch.utils.normalize(advantages, epsilon=1e-5).view(-1, 1)
for i, sars in enumerate(replay):
sars.reward = rewards[i].detach()
sars.advantage = advantages[i].detach()
# Logging
policy_losses = []
entropies = []
value_losses = []
mean = lambda a: sum(a) / len(a)
# Perform some optimization steps
for step in range(PPO_EPOCHS * PPO_NUM_BATCHES):
batch = replay.sample(PPO_BSZ)
masses, values = policy(batch.state())
# Compute losses
advs = ch.normalize(batch.advantage(), epsilon=1e-8)
new_log_probs = masses.log_prob(batch.action()).sum(-1, keepdim=True)
entropy = masses.entropy().sum(-1).mean()
policy_loss = ppo.policy_loss(
new_log_probs,
batch.log_prob(),
# batch.advantage(),
advs,
clip=PPO_CLIP)
value_loss = ppo.state_value_loss(values,
batch.value().detach(),
batch.reward(),
clip=PPO_CLIP)
loss = policy_loss - ENT_WEIGHT * entropy + V_WEIGHT * value_loss
# Take optimization step
optimizer.zero_grad()
loss.backward()
th.nn.utils.clip_grad_norm_(policy.parameters(), GRAD_NORM)
optimizer.step()
policy_losses.append(policy_loss.item())
entropies.append(entropy.item())
value_losses.append(value_loss.item())
# Log metrics
env.log('policy loss', mean(policy_losses))
env.log('policy entropy', mean(entropies))
env.log('value loss', mean(value_losses))
# Update the parameters on schedule
if LINEAR_SCHEDULE:
lr_schedule.step()
def main(
experiment='dev',
env_name='2DNavigation-v0',
adapt_lr=0.1,
meta_lr=0.01,
adapt_steps=1,
num_iterations=20,
meta_bsz=10,
adapt_bsz=10,
tau=1.00,
gamma=0.99,
num_workers=1,
seed=42,
):
random.seed(seed)
np.random.seed(seed)
th.manual_seed(seed)
def make_env():
return gym.make(env_name)
env = l2l.gym.AsyncVectorEnv([make_env for _ in range(num_workers)])
env.seed(seed)
env = ch.envs.Torch(env)
policy = DiagNormalPolicy(env.state_size, env.action_size)
meta_learner = l2l.MAML(policy, lr=meta_lr)
baseline = LinearValue(env.state_size, env.action_size)
opt = optim.Adam(meta_learner.parameters(), lr=meta_lr)
all_rewards = []
for iteration in range(num_iterations):
iteration_reward = 0.0
iteration_replays = []
iteration_policies = []
policy.to('cpu')
baseline.to('cpu')
for task_config in tqdm(env.sample_tasks(meta_bsz),
leave=False,
desc='Data'): # Samples a new config
learner = meta_learner.clone()
env.reset_task(task_config)
env.reset()
task = ch.envs.Runner(env)
task_replay = []
# Fast Adapt
for step in range(adapt_steps):
train_episodes = task.run(learner, episodes=adapt_bsz)
learner = fast_adapt_a2c(learner,
train_episodes,
adapt_lr,
baseline,
gamma,
tau,
first_order=True)
task_replay.append(train_episodes)
# Compute Validation Loss
valid_episodes = task.run(learner, episodes=adapt_bsz)
task_replay.append(valid_episodes)
iteration_reward += valid_episodes.reward().sum().item(
) / adapt_bsz
iteration_replays.append(task_replay)
iteration_policies.append(learner)
# Print statistics
print('\nIteration', iteration)
adaptation_reward = iteration_reward / meta_bsz
all_rewards.append(adaptation_reward)
print('adaptation_reward', adaptation_reward)
# PPO meta-optimization
for ppo_step in tqdm(range(10), leave=False, desc='Optim'):
ppo_loss = 0.0
for task_replays, old_policy in zip(iteration_replays,
iteration_policies):
train_replays = task_replays[:-1]
valid_replay = task_replays[-1]
# Fast adapt new policy, starting from the current init
new_policy = meta_learner.clone()
for train_episodes in train_replays:
new_policy = fast_adapt_a2c(new_policy, train_episodes,
adapt_lr, baseline, gamma, tau)
# Compute PPO loss between old and new clones
states = valid_replay.state()
actions = valid_replay.action()
rewards = valid_replay.reward()
dones = valid_replay.done()
next_states = valid_replay.next_state()
old_log_probs = old_policy.log_prob(states, actions).detach()
new_log_probs = new_policy.log_prob(states, actions)
advantages = compute_advantages(baseline, tau, gamma, rewards,
dones, states, next_states)
advantages = ch.normalize(advantages).detach()
ppo_loss += ppo.policy_loss(new_log_probs,
old_log_probs,
advantages,
clip=0.1)
ppo_loss /= meta_bsz
opt.zero_grad()
ppo_loss.backward()
opt.step()
