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@@ -60,7 +60,7 @@ class GymEnvironmentWrapper(EnvironmentWrapper):
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self.env.frameskip = self.frame_skip
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self.discrete_controls = type(self.env.action_space) != gym.spaces.box.Box
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self.observation = self.reset(True)['observation']
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self.state = self.reset(True)['state']
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# render
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if self.is_rendered:
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@@ -70,12 +70,13 @@ class GymEnvironmentWrapper(EnvironmentWrapper):
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scale = 2
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self.renderer.create_screen(image.shape[1]*scale, image.shape[0]*scale)
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self.is_state_type_image = len(self.observation.shape) > 1
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# TODO: collect and store this as observation space instead
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self.is_state_type_image = len(self.state['observation'].shape) > 1
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if self.is_state_type_image:
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self.width = self.observation.shape[1]
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self.height = self.observation.shape[0]
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self.width = self.state['observation'].shape[1]
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self.height = self.state['observation'].shape[0]
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else:
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self.width = self.observation.shape[0]
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self.width = self.state['observation'].shape[0]
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# action space
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self.actions_description = {}
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@@ -101,6 +102,12 @@ class GymEnvironmentWrapper(EnvironmentWrapper):
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self.timestep_limit = None
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self.measurements_size = len(self.step(0)['info'].keys())
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def _wrap_state(self, state):
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if isinstance(self.env.observation_space, gym.spaces.Dict):
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return state
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else:
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return {'observation': state}
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def _update_state(self):
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if hasattr(self.env, 'env') and hasattr(self.env.env, 'ale'):
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if self.phase == RunPhase.TRAIN and hasattr(self, 'current_ale_lives'):
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@@ -131,28 +138,30 @@ class GymEnvironmentWrapper(EnvironmentWrapper):
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action = np.squeeze(action)
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action = np.clip(action, self.action_space_low, self.action_space_high)
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self.observation, self.reward, self.done, self.info = self.env.step(action)
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state, self.reward, self.done, self.info = self.env.step(action)
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self.state = self._wrap_state(state)
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def _preprocess_observation(self, observation):
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def _preprocess_state(self, state):
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# TODO: move this into wrapper
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if any(env in self.env_id for env in ["Breakout", "Pong"]):
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# crop image
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observation = observation[34:195, :, :]
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return observation
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state['observation'] = state['observation'][34:195, :, :]
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return state
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def _restart_environment_episode(self, force_environment_reset=False):
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# prevent reset of environment if there are ale lives left
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if (hasattr(self.env, 'env') and hasattr(self.env.env, 'ale') and self.env.env.ale.lives() > 0) \
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and not force_environment_reset and not self.env._past_limit():
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return self.observation
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return self.state
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if self.seed:
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self.env.seed(self.seed)
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self.observation = self.env.reset()
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while self.observation is None:
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self.state = self._wrap_state(self.env.reset())
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while self.state is None:
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self.step(0)
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return self.observation
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return self.state
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def get_rendered_image(self):
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return self.env.render(mode='rgb_array')
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