added the learning algorithm from RL-lab

examples/pybullet/gym/trpo_tf_cartpole_bullet_gym.py

@@ -0,0 +1,48 @@
+from envs.bullet.cartpole_bullet import CartPoleBulletEnv
+from sandbox.rocky.tf.algos.trpo import TRPO
+from sandbox.rocky.tf.policies.gaussian_mlp_policy import GaussianMLPPolicy
+from sandbox.rocky.tf.envs.base import TfEnv
+
+from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
+from rllab.envs.gym_env import GymEnv
+from rllab.envs.normalized_env import normalize
+from rllab.misc.instrument import stub, run_experiment_lite
+
+stub(globals())
+
+env = TfEnv(normalize(GymEnv("CartPoleBulletEnv-v0")))
+
+policy = GaussianMLPPolicy(
+    name = "tf_gaussian_mlp",
+    env_spec=env.spec,
+    # The neural network policy should have two hidden layers, each with 32 hidden units.
+    hidden_sizes=(8,)
+)
+
+baseline = LinearFeatureBaseline(env_spec=env.spec)
+
+algo = TRPO(
+    env=env,
+    policy=policy,
+    baseline=baseline,
+    batch_size=5000,
+    max_path_length=env.horizon,
+    n_itr=50,
+    discount=0.999,
+    step_size=0.01,
+    force_batch_sampler=True,
+    # Uncomment both lines (this and the plot parameter below) to enable plotting
+    #plot=True,
+)
+
+run_experiment_lite(
+    algo.train(),
+    # Number of parallel workers for sampling
+    n_parallel=1,
+    # Only keep the snapshot parameters for the last iteration
+    snapshot_mode="last",
+    # Specifies the seed for the experiment. If this is not provided, a random seed
+    # will be used
+    seed=1,
+    #plot=True,
+)