32 lines
876 B
Python
32 lines
876 B
Python
from envs.bullet.cartpole_bullet import CartPoleBulletEnv
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from rllab.algos.trpo import TRPO
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from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
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from rllab.envs.gym_env import GymEnv
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from rllab.envs.normalized_env import normalize
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from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
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env = normalize(GymEnv("CartPoleBulletEnv-v0"))
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policy = GaussianMLPPolicy(
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env_spec=env.spec,
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# The neural network policy should have two hidden layers, each with 32 hidden units.
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hidden_sizes=(8,)
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)
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baseline = LinearFeatureBaseline(env_spec=env.spec)
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algo = TRPO(
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env=env,
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policy=policy,
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baseline=baseline,
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batch_size=5000,
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max_path_length=env.horizon,
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n_itr=50,
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discount=0.999,
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step_size=0.01,
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# Uncomment both lines (this and the plot parameter below) to enable plotting
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# plot=True,
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)
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algo.train()
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