Merge updated files

scratch/etienne/trpo/experiments/wgail-ppo-options-setobs2.py

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+import gym
+from options.options import gail_ppo, Buffer
+from core.value import SetValue
+from core.policy import SetDiscretePolicy
+from core.discriminator import DeepsetDiscriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, TransformObservation, Setobs
+import numpy as np
+from options.options import OptionsEnv
+from torch.utils.tensorboard import SummaryWriter
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [OptionsEnv(Setobs(
+    TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
+        n_rays=5,
+        reward=functools.partial(
+            speed_reward,
+            collision_penalty=0
+        ),
+        stop_on_collision=False,
+    ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
+), options=[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5)]) for _ in range(60)]
+
+env_fn = lambda i: envs[i]
+
+policy = SetDiscretePolicy(env_fn(0).action_space.n)
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+
+value = SetValue()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+
+discriminator = DeepsetDiscriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-4)
+
+expert_data = torch.load('intersimple-expert-data-setobs2.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail_ppo(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=100,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=200,
+    rollout_episodes=60,
+    rollout_steps=60,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+    wasserstein=True,
+    wasserstein_c=1.,
+    logger=SummaryWriter(comment='wgail-ppo-options-setobs2'),
+)
+
+torch.save(policy.state_dict(), 'wgail-ppo-options-setobs2.pt')
+
+# %%
+policy = SetDiscretePolicy(env_fn(0).action_space.n)
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy.load_state_dict(torch.load('wgail-ppo-options-setobs2.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action, render_mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()