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Port TRPO, PPO, GAIL

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ebuehrle
2022-02-15 11:01:52 +01:00
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scratch/etienne/trpo/options/options.py Normal file
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import gym
import numpy as np
import torch
from stable_baselines3.common.vec_env import DummyVecEnv as VecEnv
from core.reparam_module import ReparamPolicy
from tqdm import tqdm
from core.gail import Buffer, train_discriminator, roll_buffer, TerminalLogger
from dataclasses import dataclass
from core.trpo import trpo_step
from core.ppo import ppo_step
import torch.nn.functional as F
@dataclass
class OptionsRollout:
hl: Buffer
ll: Buffer
def gail(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, value,
v_opt, v_iters, epochs, rollout_episodes, rollout_steps, gamma,
gae_lambda, delta, backtrack_coeff, backtrack_iters, cg_iters=10, cg_damping=0.1, wasserstein=False, wasserstein_c=None, logger=TerminalLogger()):
policy(torch.zeros(env_fn(0).observation_space.shape))
policy = ReparamPolicy(policy)
logger.add_scalar('expert/mean_episode_length', (~expert_data.dones).sum() / expert_data.states.shape[0])
logger.add_scalar('expert/mean_reward_per_episode', expert_data.rewards[~expert_data.dones].sum() / expert_data.states.shape[0])
for epoch in tqdm(range(epochs)):
hl_data, ll_data = rollout(env_fn, policy, rollout_episodes, rollout_steps)
generator_data = OptionsRollout(Buffer(*hl_data), Buffer(*ll_data))
generator_data.ll.actions += 0.1 * torch.randn_like(generator_data.ll.actions)
logger.add_scalar('gen/mean_episode_length', (~generator_data.ll.dones).sum() / generator_data.ll.states.shape[0], epoch)
logger.add_scalar('gen/mean_reward_per_episode', generator_data.hl.rewards[~generator_data.hl.dones].sum() / generator_data.hl.states.shape[0], epoch)
discriminator, loss = train_discriminator(expert_data, generator_data.ll, discriminator, disc_opt, disc_iters, wasserstein, wasserstein_c)
if wasserstein:
generator_data.ll.rewards = discriminator(generator_data.ll.states, generator_data.ll.actions)
else:
generator_data.ll.rewards = -F.logsigmoid(discriminator(generator_data.ll.states, generator_data.ll.actions))
logger.add_scalar('disc/final_loss', loss, epoch)
logger.add_scalar('disc/mean_reward_per_episode', generator_data.ll.rewards[~generator_data.ll.dones].sum() / generator_data.ll.states.shape[0], epoch)
#assert generator_data.ll.rewards.shape == generator_data.ll.dones.shape
generator_data.hl.rewards = torch.where(~generator_data.ll.dones, generator_data.ll.rewards, torch.tensor(0.)).sum(-1)
value, policy = trpo_step(value, policy, generator_data.hl.states, generator_data.hl.actions, generator_data.hl.rewards, generator_data.hl.dones, gamma, gae_lambda, delta, backtrack_coeff, backtrack_iters, v_opt, v_iters, cg_iters, cg_damping)
expert_data = roll_buffer(expert_data, shifts=-3, dims=0)
return value, policy
def gail_ppo(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, value,
v_opt, v_iters, epochs, rollout_episodes, rollout_steps, gamma,
gae_lambda, clip_ratio, pi_opt, pi_iters, target_kl=None, max_grad_norm=None, wasserstein=False, wasserstein_c=None, logger=TerminalLogger()):
logger.add_scalar('expert/mean_episode_length', (~expert_data.dones).sum() / expert_data.states.shape[0])
logger.add_scalar('expert/mean_reward_per_episode', expert_data.rewards[~expert_data.dones].sum() / expert_data.states.shape[0])
for epoch in range(epochs):
hl_data, ll_data = rollout(env_fn, policy, rollout_episodes, rollout_steps)
generator_data = OptionsRollout(Buffer(*hl_data), Buffer(*ll_data))
generator_data.ll.actions += 0.1 * torch.randn_like(generator_data.ll.actions)
logger.add_scalar('gen/mean_episode_length', (~generator_data.ll.dones).sum() / generator_data.ll.states.shape[0], epoch)
logger.add_scalar('gen/mean_reward_per_episode', generator_data.hl.rewards[~generator_data.hl.dones].sum() / generator_data.hl.states.shape[0], epoch)
discriminator, loss = train_discriminator(expert_data, generator_data.ll, discriminator, disc_opt, disc_iters, wasserstein, wasserstein_c)
if wasserstein:
generator_data.ll.rewards = discriminator(generator_data.ll.states, generator_data.ll.actions)
else:
generator_data.ll.rewards = -F.logsigmoid(discriminator(generator_data.ll.states, generator_data.ll.actions))
logger.add_scalar('disc/final_loss', loss, epoch)
logger.add_scalar('disc/mean_reward_per_episode', generator_data.ll.rewards[~generator_data.ll.dones].sum() / generator_data.ll.states.shape[0], epoch)
#assert generator_data.ll.rewards.shape == generator_data.ll.dones.shape
generator_data.hl.rewards = torch.where(~generator_data.ll.dones, generator_data.ll.rewards, torch.tensor(0.)).sum(-1)
value, policy = ppo_step(value, policy, generator_data.hl.states, generator_data.hl.actions, generator_data.hl.rewards, generator_data.hl.dones, clip_ratio, gamma, gae_lambda, pi_opt, pi_iters, v_opt, v_iters, target_kl, max_grad_norm)
expert_data = roll_buffer(expert_data, shifts=-3, dims=0)
return value, policy
def rollout(env_fn, policy, n_episodes, max_steps_per_episode):
env = env_fn(0)
states = torch.zeros(n_episodes, max_steps_per_episode + 1, *env.observation_space.shape)
actions = torch.zeros(n_episodes, max_steps_per_episode + 1, *env.action_space.shape)
rewards = torch.zeros(n_episodes, max_steps_per_episode + 1)
dones = torch.ones(n_episodes, max_steps_per_episode + 1, dtype=bool)
ll_states = torch.zeros(n_episodes, max_steps_per_episode, env.max_plan_length + 1, *env.observation_space.shape)
ll_actions = torch.zeros(n_episodes, max_steps_per_episode, env.max_plan_length + 1, *env.ll_action_space.shape)
ll_rewards = torch.zeros(n_episodes, max_steps_per_episode, env.max_plan_length + 1)
ll_dones = torch.ones(n_episodes, max_steps_per_episode, env.max_plan_length + 1, dtype=bool)
env = VecEnv(list(map(lambda i: (lambda: env_fn(i)), range(n_episodes))))
states[:, 0] = torch.tensor(env.reset()).clone().detach()
dones[:, 0] = False
for s in tqdm(range(max_steps_per_episode), 'Rollout'):
actions[:, s] = policy.sample(policy(states[:, s])).clone().detach()
clipped_actions = actions[:, s]
if isinstance(env.action_space, gym.spaces.Box):
clipped_actions = torch.clamp(clipped_actions, torch.from_numpy(env.action_space.low), torch.from_numpy(env.action_space.high))
o, r, d, info = env.step(clipped_actions)
states[:, s + 1] = torch.tensor(o).clone().detach()
rewards[:, s] = torch.tensor(r).clone().detach()
dones[:, s + 1] = torch.tensor(d).clone().detach()
ll_states[:, s] = torch.from_numpy(np.stack([i['ll']['observations'] for i in info])).clone().detach()
ll_actions[:, s] = torch.from_numpy(np.stack([i['ll']['actions'] for i in info])).clone().detach()
ll_rewards[:, s] = torch.from_numpy(np.stack([i['ll']['rewards'] for i in info])).clone().detach()
ll_dones[:, s] = torch.from_numpy(np.stack([i['ll']['plan_done'] for i in info])).clone().detach()
dones = dones.cumsum(1) > 0
states = states[:, :max_steps_per_episode]
actions = actions[:, :max_steps_per_episode]
rewards = rewards[:, :max_steps_per_episode]
dones = dones[:, :max_steps_per_episode]
return (states, actions, rewards, dones), (ll_states, ll_actions, ll_rewards, ll_dones)
class OptionsEnv(gym.Wrapper):
def __init__(self, env, options):
super().__init__(env)
self.ll_action_space = env.action_space
self.options = options
self.action_space = gym.spaces.Discrete(len(options))
self.max_plan_length = max(t for _, t in options)
def plan(self, option):
target_v, t = option
current_v = self.env._env.state[self.env._agent, 1].item()
dt = self.env._env._dt
a = (target_v - current_v) / (t * dt)
a = self.env._normalize(a)
a = a * np.ones((t,))
a += 0.01 * np.random.randn(*a.shape)
a = np.clip(a, self.ll_action_space.low, self.ll_action_space.high)
return a
def execute_plan(self, obs, option, render_mode=None):
observations = np.zeros((self.max_plan_length + 1, *self.env.observation_space.shape))
actions = np.zeros((self.max_plan_length + 1, *self.ll_action_space.shape))
rewards = np.zeros((self.max_plan_length + 1,))
env_done = np.ones((self.max_plan_length + 1,), dtype=bool)
plan_done = np.ones((self.max_plan_length + 1,), dtype=bool)
infos = []
observations[0] = obs
env_done[0] = False
for k, u in enumerate(self.plan(option)):
plan_done[k] = False
o, r, d, i = super().step(u)
actions[k] = u
rewards[k] = r
env_done[k+1] = d
infos.append(i)
observations[k+1] = o
if render_mode is not None:
self.env.render(render_mode)
if d:
break
n_steps = k + 1
return observations, actions, rewards, env_done, plan_done, infos, n_steps
def step(self, action, render_mode=None):
a = int(action)
assert a == action
ll_obs, ll_actions, ll_rewards, ll_env_done, ll_plan_done, ll_infos, ll_steps = self.execute_plan(self.last_obs, self.options[a], render_mode)
hl_obs = ll_obs[ll_steps]
hl_reward = (ll_rewards * ~ll_plan_done).sum().item()
hl_done = ll_env_done[ll_steps].item()
hl_infos = {
'll': {
'observations': ll_obs,
'actions': ll_actions,
'rewards': ll_rewards,
'env_done': ll_env_done,
'plan_done': ll_plan_done,
'infos': ll_infos,
'steps': ll_steps,
}
}
self.last_obs = hl_obs
return hl_obs, hl_reward, hl_done, hl_infos
def reset(self, *args, **kwargs):
self.last_obs = super().reset(*args, **kwargs)
return self.last_obs