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[rllib] Support discrete observation spaces such as FrozenLake-v0 (#1140)
* add * remove transform_shape * fix test * fix
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@@ -74,10 +74,7 @@ class Worker(object):
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self.noise = SharedNoiseTable(noise)
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self.env = env_creator()
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self.preprocessor = ModelCatalog.get_preprocessor(
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self.env.spec.id, self.env.observation_space.shape)
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self.preprocessor_shape = self.preprocessor.transform_shape(
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self.env.observation_space.shape)
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self.preprocessor = ModelCatalog.get_preprocessor(self.env)
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self.sess = utils.make_session(single_threaded=True)
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self.policy = policies.GenericPolicy(
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@@ -118,7 +115,7 @@ class Worker(object):
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noise_inds, returns, sign_returns, lengths = [], [], [], []
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# We set eps=0 because we're incrementing only.
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task_ob_stat = utils.RunningStat(self.preprocessor_shape, eps=0)
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task_ob_stat = utils.RunningStat(self.preprocessor.shape, eps=0)
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# Perform some rollouts with noise.
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task_tstart = time.time()
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@@ -169,10 +166,7 @@ class ESAgent(Agent):
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}
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env = self.env_creator()
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preprocessor = ModelCatalog.get_preprocessor(
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env.spec.id, env.observation_space.shape)
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preprocessor_shape = preprocessor.transform_shape(
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env.observation_space.shape)
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preprocessor = ModelCatalog.get_preprocessor(env)
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self.sess = utils.make_session(single_threaded=False)
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self.policy = policies.GenericPolicy(
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@@ -180,7 +174,7 @@ class ESAgent(Agent):
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**policy_params)
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tf_util.initialize()
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self.optimizer = optimizers.Adam(self.policy, self.config["stepsize"])
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self.ob_stat = utils.RunningStat(preprocessor_shape, eps=1e-2)
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self.ob_stat = utils.RunningStat(preprocessor.shape, eps=1e-2)
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# Create the shared noise table.
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print("Creating shared noise table.")
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