coach v0.8.0

memories/memory.py

@@ -0,0 +1,135 @@
+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import copy
+from configurations import *
+
+
+class Memory:
+    def __init__(self, tuning_parameters):
+        """
+        :param tuning_parameters: A Preset class instance with all the running paramaters
+        :type tuning_parameters: Preset
+        """
+        pass
+
+    def store(self, obj):
+        pass
+
+    def get(self, index):
+        pass
+
+    def length(self):
+        pass
+
+    def sample(self, size):
+        pass
+
+    def clean(self):
+        pass
+
+
+class Episode:
+    def __init__(self):
+        self.transitions = []
+        # a num_transitions x num_transitions table with the n step return in the n'th row
+        self.returns_table = None
+        self._length = 0
+
+    def insert(self, transition):
+        self.transitions.append(transition)
+        self._length += 1
+
+    def is_empty(self):
+        return self.length() == 0
+
+    def length(self):
+        return self._length
+
+    def get_transition(self, transition_idx):
+        return self.transitions[transition_idx]
+
+    def get_last_transition(self):
+        return self.get_transition(-1)
+
+    def get_first_transition(self):
+        return self.get_transition(0)
+
+    def update_returns(self, discount, is_bootstrapped=False, n_step_return=-1):
+        if n_step_return == -1 or n_step_return > self.length():
+            n_step_return = self.length()
+        rewards = np.array([t.reward for t in self.transitions])
+        total_return = rewards.copy()
+        current_discount = discount
+        for i in range(1, n_step_return):
+            total_return += current_discount * np.pad(rewards[i:], (0, i), 'constant', constant_values=0)
+            current_discount *= discount
+
+        if is_bootstrapped:
+            bootstraps = np.array([np.squeeze(t.info['action_value']) for t in self.transitions[n_step_return:]])
+            total_return += current_discount * np.pad(bootstraps, (0, n_step_return), 'constant', constant_values=0)
+
+        for transition_idx in range(self.length()):
+            self.transitions[transition_idx].total_return = total_return[transition_idx]
+
+    def update_measurements_targets(self, num_steps):
+        if 'measurements' not in self.transitions[0].state:
+            return
+        measurements_size = self.transitions[0].state['measurements'].shape[-1]
+        total_return = sum([transition.reward for transition in self.transitions])
+        for transition_idx, transition in enumerate(self.transitions):
+            transition.info['future_measurements'] = np.zeros((num_steps, measurements_size))
+            for step in range(num_steps):
+                offset_idx = transition_idx + 2 ** step
+                if offset_idx >= self.length():
+                    offset_idx = -1
+                transition.info['future_measurements'][step] = self.transitions[offset_idx].next_state['measurements'] - \
+                                                               transition.state['measurements']
+            transition.info['total_episode_return'] = total_return
+
+    def update_actions_probabilities(self):
+        probability_product = 1
+        for transition_idx, transition in enumerate(self.transitions):
+            if 'action_probabilities' in transition.info.keys():
+                probability_product *= transition.info['action_probabilities']
+        for transition_idx, transition in enumerate(self.transitions):
+            transition.info['probability_product'] = probability_product
+
+    def get_returns_table(self):
+        return self.returns_table
+
+    def get_returns(self):
+        return [t.total_return for t in self.transitions]
+
+    def to_batch(self):
+        batch = []
+        for i in range(self.length()):
+            batch.append(self.get_transition(i))
+        return batch
+
+
+class Transition:
+    def __init__(self, state, action, reward, next_state, game_over):
+        self.state = copy.deepcopy(state)
+        self.state['observation'] = np.array(self.state['observation'], copy=False)
+        self.action = action
+        self.reward = reward
+        self.total_return = None
+        self.next_state = copy.deepcopy(next_state)
+        self.next_state['observation'] = np.array(self.next_state['observation'], copy=False)
+        self.game_over = game_over
+        self.info = {}