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66 lines
2.5 KiB
Python
66 lines
2.5 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from collections import OrderedDict
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from typing import Union
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from rl_coach.agents.agent import Agent
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from rl_coach.core_types import RunPhase, ActionInfo
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from rl_coach.logger import screen
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from rl_coach.spaces import DiscreteActionSpace
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## This is an abstract agent - there is no learn_from_batch method ##
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# Imitation Agent
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class ImitationAgent(Agent):
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def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
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super().__init__(agent_parameters, parent)
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self.imitation = True
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def extract_action_values(self, prediction):
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return prediction.squeeze()
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def choose_action(self, curr_state):
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# convert to batch so we can run it through the network
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prediction = self.networks['main'].online_network.predict(self.prepare_batch_for_inference(curr_state, 'main'))
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# get action values and extract the best action from it
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action_values = self.extract_action_values(prediction)
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self.exploration_policy.change_phase(RunPhase.TEST)
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action = self.exploration_policy.get_action(action_values)
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action_info = ActionInfo(action=action)
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return action_info
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def log_to_screen(self):
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# log to screen
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if self.phase == RunPhase.TRAIN:
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# for the training phase - we log during the episode to visualize the progress in training
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log = OrderedDict()
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if self.task_id is not None:
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log["Worker"] = self.task_id
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log["Episode"] = self.current_episode
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log["Loss"] = self.loss.values[-1]
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log["Training iteration"] = self.training_iteration
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screen.log_dict(log, prefix="Training")
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else:
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# for the evaluation phase - logging as in regular RL
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super().log_to_screen()
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def learn_from_batch(self, batch):
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raise NotImplementedError("ImitationAgent is an abstract agent. Not to be used directly.")
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