Port TRPO, PPO, GAIL

scratch/etienne/trpo/gail-pendulum.py

@@ -0,0 +1,39 @@
+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
+
+env_fn = lambda _: gym.make('Pendulum-v0')
+policy = Policy(env_fn(0).action_space.shape[0])
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+discriminator = Discriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3)
+
+expert_data = torch.load('trpo-pendulum-expert-data.pt')
+expert_data = Buffer(*expert_data)
+
+gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=10,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=100,
+    rollout_episodes=20,
+    rollout_steps=250,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+)
+
+
+torch.save(policy.state_dict(), 'gail-pendulum.pt')