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136 lines
4.8 KiB
Python
136 lines
4.8 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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import numpy as np
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import copy
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from configurations import *
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class Memory(object):
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def __init__(self, tuning_parameters):
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"""
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:param tuning_parameters: A Preset class instance with all the running paramaters
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:type tuning_parameters: Preset
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"""
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pass
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def store(self, obj):
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pass
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def get(self, index):
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pass
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def length(self):
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pass
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def sample(self, size):
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pass
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def clean(self):
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pass
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class Episode(object):
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def __init__(self):
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self.transitions = []
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# a num_transitions x num_transitions table with the n step return in the n'th row
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self.returns_table = None
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self._length = 0
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def insert(self, transition):
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self.transitions.append(transition)
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self._length += 1
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def is_empty(self):
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return self.length() == 0
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def length(self):
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return self._length
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def get_transition(self, transition_idx):
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return self.transitions[transition_idx]
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def get_last_transition(self):
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return self.get_transition(-1)
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def get_first_transition(self):
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return self.get_transition(0)
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def update_returns(self, discount, is_bootstrapped=False, n_step_return=-1):
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if n_step_return == -1 or n_step_return > self.length():
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n_step_return = self.length()
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rewards = np.array([t.reward for t in self.transitions])
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total_return = rewards.copy()
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current_discount = discount
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for i in range(1, n_step_return):
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total_return += current_discount * np.pad(rewards[i:], (0, i), 'constant', constant_values=0)
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current_discount *= discount
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if is_bootstrapped:
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bootstraps = np.array([np.squeeze(t.info['action_value']) for t in self.transitions[n_step_return:]])
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total_return += current_discount * np.pad(bootstraps, (0, n_step_return), 'constant', constant_values=0)
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for transition_idx in range(self.length()):
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self.transitions[transition_idx].total_return = total_return[transition_idx]
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def update_measurements_targets(self, num_steps):
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if 'measurements' not in self.transitions[0].state:
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return
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measurements_size = self.transitions[0].state['measurements'].shape[-1]
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total_return = sum([transition.reward for transition in self.transitions])
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for transition_idx, transition in enumerate(self.transitions):
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transition.info['future_measurements'] = np.zeros((num_steps, measurements_size))
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for step in range(num_steps):
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offset_idx = transition_idx + 2 ** step
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if offset_idx >= self.length():
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offset_idx = -1
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transition.info['future_measurements'][step] = self.transitions[offset_idx].next_state['measurements'] - \
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transition.state['measurements']
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transition.info['total_episode_return'] = total_return
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def update_actions_probabilities(self):
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probability_product = 1
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for transition_idx, transition in enumerate(self.transitions):
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if 'action_probabilities' in transition.info.keys():
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probability_product *= transition.info['action_probabilities']
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for transition_idx, transition in enumerate(self.transitions):
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transition.info['probability_product'] = probability_product
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def get_returns_table(self):
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return self.returns_table
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def get_returns(self):
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return [t.total_return for t in self.transitions]
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def to_batch(self):
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batch = []
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for i in range(self.length()):
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batch.append(self.get_transition(i))
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return batch
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class Transition(object):
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def __init__(self, state, action, reward, next_state, game_over):
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self.state = copy.deepcopy(state)
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self.state['observation'] = np.array(self.state['observation'], copy=False)
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self.action = action
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self.reward = reward
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self.total_return = None
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self.next_state = copy.deepcopy(next_state)
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self.next_state['observation'] = np.array(self.next_state['observation'], copy=False)
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self.game_over = game_over
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self.info = {}
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