Merge updated files

scratch/etienne/trpo/experiments/wgail-intersimple-minobs.py

@@ -0,0 +1,76 @@
+import gym
+from core.gail import gail, Buffer
+from core.value import Value
+from core.policy import Policy
+from core.discriminator import Discriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, Minobs
+import numpy as np
+from gym.wrappers import TransformObservation
+
+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 = [Minobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    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))) for _ in range(30)]
+env_fn = lambda i: envs[i]
+
+policy = Policy(env_fn(0).action_space.shape[0])
+
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+discriminator = Discriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-2)
+
+expert_data = torch.load('intersimple-expert-data-minobs.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail(
+    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=4000,
+    rollout_episodes=30,
+    rollout_steps=200,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+    wasserstein=True,
+    wasserstein_c=1.,
+)
+
+torch.save(policy.state_dict(), 'wgail-intersimple-minobs.pt')