Make options env compatible with PPO
This commit is contained in:
ebuehrle
@@ -2,13 +2,22 @@ import gym
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import torch
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from src.util.collisions import feasible
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import numpy as np
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from collections import deque
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import itertools
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def imitation_discriminator(discriminator):
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return lambda obs, action, next_obs, done: discriminator.discrim_net.predict_reward_train(
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state=torch.tensor(obs).unsqueeze(0).to(discriminator.discrim_net.device()),
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action=torch.tensor([[action]]).to(discriminator.discrim_net.device()),
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next_state=torch.tensor(next_obs).unsqueeze(0).to(discriminator.discrim_net.device()), # unused
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done=torch.tensor(done).unsqueeze(0).to(discriminator.discrim_net.device()), # unused
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).item()
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class OptionsEnv(gym.Wrapper):
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def __init__(self, env, options=[(0, 5), (5, 5), (10, 5)], *args, **kwargs):
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"""option 0 is treated as safe fallback"""
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def __init__(self, env, options, discriminator, ll_buffer_capacity, *args, **kwargs):
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super().__init__(env, *args, **kwargs)
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self.options = options
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num_hl_options = len(self.options)
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self.action_space = gym.spaces.Discrete(num_hl_options)
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@@ -17,118 +26,72 @@ class OptionsEnv(gym.Wrapper):
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'mask': gym.spaces.Box(low=0, high=1, shape=(num_hl_options,)),
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})
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def _after_choice(self):
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pass
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self.discriminator = discriminator
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self.ll_buffer_capacity = ll_buffer_capacity
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self.ll_buffer = deque(maxlen=ll_buffer_capacity)
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def _after_step(self):
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pass
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def _transitions(self):
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raise NotImplementedError('Use `LLOptions` or `HLOptions` for sampling.')
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def sample(self, generator):
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self.done = True
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while True:
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self.episode_start = False
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if self.done:
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self.s = self.env.reset()
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self.done = False
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self.episode_start = True
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self.m = available_actions(self.env, self.options)
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if not self.m.any():
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# action 0 is considered safe fallback
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self.m[0] = True
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self.ch, self.value, self.log_prob = generator.policy.forward({
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'obs': torch.tensor(self.s).unsqueeze(0).to(generator.policy.device),
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'mask': self.m.unsqueeze(0).to(generator.policy.device),
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})
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self.plan = list(map(float, generate_plan(self.env, self.ch, self.options)))
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self._after_choice()
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assert not self.done
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assert self.plan
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#assert feasible(self.env, self.plan, self.ch)
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while not self.done and self.plan and \
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(feasible(self.env, safety_plan(self.env, self.plan)) or self.m.sum() == 1):
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self.a, self.plan = self.plan[0], self.plan[1:]
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self.a = self.env._normalize(self.a)
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self.nexts, _, self.done, _ = self.env.step(self.a)
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self._after_step()
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self.s = self.nexts
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yield from self._transitions()
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class LLOptions(OptionsEnv):
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"""Sample low-level (state, action) tuples for discriminator training."""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.observation_space = self.observation_space['obs']
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def _after_choice(self):
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self._transition_buffer = []
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def _after_step(self):
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self._transition_buffer.append({
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'obs': self.s,
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'next_obs': self.nexts,
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'acts': np.array((self.a,)),
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'dones': np.array(self.done),
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})
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def _transitions(self):
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yield from self._transition_buffer
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def sample_ll(self, policy):
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return self.sample(policy)
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class HLOptions(OptionsEnv):
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"""Sample high-level (state, action, reward) tuples for generator training."""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def _after_choice(self):
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self.obs = {'obs': np.copy(self.s), 'mask': np.copy(self.m)}
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self.r = 0
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self.steps = 0
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def _after_step(self):
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self.r += self.discount**self.steps * self.discriminator.discrim_net.predict_reward_train(
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state=torch.tensor(self.s).unsqueeze(0).to(self.discriminator.discrim_net.device()),
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action=torch.tensor([[self.a]]).to(self.discriminator.discrim_net.device()),
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next_state=torch.tensor(self.s).unsqueeze(0).to(self.discriminator.discrim_net.device()), # unused
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done=torch.tensor(self.done).unsqueeze(0).to(self.discriminator.discrim_net.device()), # unused
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)
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self.steps += 1
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def _transitions(self):
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yield {
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'obs': self.obs,
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'action': self.ch.cpu(),
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'reward': self.r,
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'episode_start': self.episode_start,
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'value': self.value.detach(),
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'log_prob': self.log_prob.detach(),
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'done': self.done,
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@staticmethod
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def _hl_observation(obs, mask):
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return {
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'obs': obs,
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'mask': mask,
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}
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def sample_hl(self, policy, discriminator):
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self.discriminator = discriminator
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return self.sample(policy)
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def reset(self):
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self.done = False
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self.obs = self.env.reset()
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self.m = available_actions(self.env, self.options)
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return self._hl_observation(self.obs, self.m)
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def _ll_step(self, action):
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return self.env.step(action)
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def step(self, action):
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assert self.m[action]
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assert not self.done
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class RenderOptions(LLOptions):
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plan = list(map(float, generate_plan(self.env, action, self.options)))
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reward = 0
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steps = 0
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def _after_step(self):
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super()._after_step()
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while not self.done and plan and \
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(feasible(self.env, safety_plan(self.env, plan)) or self.m.sum() == 1):
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a, plan = plan[0], plan[1:]
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a = self.env._normalize(a)
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next_obs, _, self.done, info = self._ll_step(a)
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reward += self.discount**steps * self.discriminator(self.obs, a, next_obs, self.done)
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self.ll_buffer.append({
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'obs': self.obs,
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'next_obs': next_obs,
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'acts': np.array((a,)),
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'dones': np.array(self.done),
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})
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steps += 1
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self.obs = next_obs
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self.m = available_actions(self.env, self.options)
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return self._hl_observation(self.obs, self.m), reward, self.done, info
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def sample_ll(self, n):
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assert n <= self.ll_buffer_capacity, f'Sample size of {n} exceeds buffer capacity of {self.ll_buffer_capacity}'
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assert n <= len(self.ll_buffer), f'Sample size of {n} exceeds buffer size of {len(self.ll_buffer)}'
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return list(itertools.islice(self.ll_buffer, n))
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class RenderOptions(OptionsEnv):
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def __init__(self, options, *args, **kwargs):
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super().__init__(options, discriminator=lambda s, a, n, d: 0, ll_buffer_capacity=0, *args, **kwargs)
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def _ll_step(self):
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out = super()._ll_step()
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self.env.render()
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return out
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def close(self, *args, **kwargs):
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self.env.close(*args, **kwargs)
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@@ -137,7 +100,9 @@ def safety_plan(env, plan):
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return np.concatenate((plan, np.array(5 * [env._env._min_acc])), axis=0)
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def available_actions(env, options):
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"""Return mask of available actions given current `env` state."""
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"""Return mask of available actions given current `env` state.
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Action 0 is considered safe fallback.
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"""
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plans = [generate_plan(env, i, options) for i, _ in enumerate(options)]
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# is emergency braking still possible?
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plans = list(map(lambda p: safety_plan(env, p), plans))
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@@ -147,6 +112,9 @@ def available_actions(env, options):
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plans = np.stack(plans, axis=0)
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valid = feasible(env, plans)
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if not valid.any():
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valid[0] = True
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return valid
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def target_velocity_plan(current_v: float, target_v: float, t: int, dt: float):
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@@ -20,8 +20,9 @@ from imitation.util import logger
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from stable_baselines3.common.env_util import make_vec_env
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from tqdm import tqdm
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from src.policies.options import OptionsCnnPolicy
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from src.gail.options import OptionsEnv, LLOptions, HLOptions, RenderOptions
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from src.gail.train import train_discriminator, train_generator
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from src.gail.train import flatten_transitions
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from gail.options2 import OptionsEnv, RenderOptions, imitation_discriminator
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model_name = 'gail_options_image_random_location'
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env_settings = {'width': 70, 'height': 70, 'm_per_px': 1, 'map_color': 128, 'mu': 0.001}
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@@ -30,12 +31,13 @@ ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10, 20]] # option 0 is sa
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def train(
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expert_data,
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epochs=200,
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expert_batch_size=256,
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generator_steps=1024,
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discount=0.99,
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n_disc_updates_per_round=2,
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generator_steps=512,
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generator_total_steps=2048,
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n_gen_updates_per_round=10,
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discount=0.99,
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epochs=100,
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):
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env = NRasterizedRouteRandomAgentLocation(**env_settings)
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env.discount = discount
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@@ -55,24 +57,25 @@ def train(
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gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
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)
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options_env = OptionsEnv(env, discriminator=imitation_discriminator(discriminator), options=ALL_OPTIONS, ll_buffer_capacity=expert_batch_size)
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generator = stable_baselines3.PPO(
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OptionsCnnPolicy,
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OptionsEnv(env, options=ALL_OPTIONS),
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options_env,
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verbose=1,
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n_steps=generator_steps,
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n_epochs=n_gen_updates_per_round,
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)
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# PPO.train requires logger as set up in
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# PPO._setup_learn (called by PPO.learn)
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generator._logger = stable_baselines3.common.utils.configure_logger(
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generator.verbose,
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generator.tensorboard_log,
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)
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for _ in tqdm(range(epochs)):
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train_discriminator(LLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=expert_batch_size, n_updates=n_disc_updates_per_round)
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train_generator(HLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=generator_steps)
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# train generator
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generator.learn(total_timesteps=generator_total_steps)
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# train discriminator
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generator_samples = options_env.sample_ll(expert_batch_size)
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generator_samples = flatten_transitions(generator_samples)
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for _ in range(n_disc_updates_per_round):
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discriminator.train_disc(gen_samples=generator_samples)
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generator.save(model_name)
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return generator
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