Python baselines.common.segment_tree.MinSegmentTree() Examples
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Example #1
Source File: replay_buffers.py From qmap with MIT License | 6 votes |
def __init__(self, size, alpha, epsilon, timesteps, initial_p, final_p): super(DoublePrioritizedReplayBuffer, self).__init__(size) assert alpha > 0 self._alpha = alpha self._epsilon = epsilon self._beta_schedule = LinearSchedule(timesteps, initial_p=initial_p, final_p=final_p) it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0 self._it_sum2 = SumSegmentTree(it_capacity) self._it_min2 = MinSegmentTree(it_capacity) self._max_priority2 = 1.0
Example #2
Source File: self_imitation.py From self-imitation-learning with MIT License | 6 votes |
def __init__(self, size, alpha): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. alpha: float how much prioritization is used (0 - no prioritization, 1 - full prioritization) See Also -------- ReplayBuffer.__init__ """ super(PrioritizedReplayBuffer, self).__init__(size) assert alpha > 0 self._alpha = alpha it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #3
Source File: prioritized_memory.py From Rainbow_ddpg with MIT License | 6 votes |
def __init__(self, limit, alpha, transition_small_epsilon=1e-6, demo_epsilon=0.2, nb_rollout_steps=100): super(PrioritizedMemory, self).__init__(limit, nb_rollout_steps) assert alpha > 0 self._alpha = alpha self._transition_small_epsilon = transition_small_epsilon self._demo_epsilon = demo_epsilon it_capacity = 1 while it_capacity < self.maxsize: it_capacity *= 2 # Size must be power of 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #4
Source File: test_segment_tree.py From baselines with MIT License | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #5
Source File: test_segment_tree.py From rl-attack-detection with MIT License | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #6
Source File: replay_buffer.py From sonic_contest with MIT License | 5 votes |
def __init__(self, size, alpha): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. alpha: float how much prioritization is used (0 - no prioritization, 1 - full prioritization) See Also -------- ReplayBuffer.__init__ """ super(PrioritizedReplayBuffer, self).__init__(size) assert alpha >= 0 self._alpha = alpha it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #7
Source File: test_segment_tree.py From sonic_contest with MIT License | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #8
Source File: replay_buffer.py From self-imitation-learning with MIT License | 5 votes |
def __init__(self, size, alpha): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. alpha: float how much prioritization is used (0 - no prioritization, 1 - full prioritization) See Also -------- ReplayBuffer.__init__ """ super(PrioritizedReplayBuffer, self).__init__(size) assert alpha >= 0 self._alpha = alpha it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #9
Source File: test_segment_tree.py From self-imitation-learning with MIT License | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #10
Source File: replay_buffer.py From baselines with MIT License | 5 votes |
def __init__(self, size, alpha): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. alpha: float how much prioritization is used (0 - no prioritization, 1 - full prioritization) See Also -------- ReplayBuffer.__init__ """ super(PrioritizedReplayBuffer, self).__init__(size) assert alpha >= 0 self._alpha = alpha it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #11
Source File: test_segment_tree.py From MOREL with MIT License | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #12
Source File: sil_module.py From self-imitation-learning-pytorch with MIT License | 5 votes |
def __init__(self, size, alpha): super(PrioritizedReplayBuffer, self).__init__(size) assert alpha > 0 self._alpha = alpha it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #13
Source File: replay_buffer.py From deeprl-baselines with MIT License | 5 votes |
def __init__(self, size, alpha): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. alpha: float how much prioritization is used (0 - no prioritization, 1 - full prioritization) See Also -------- ReplayBuffer.__init__ """ super(PrioritizedReplayBuffer, self).__init__(size) assert alpha > 0 self._alpha = alpha it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #14
Source File: test_segment_tree.py From deeprl-baselines with MIT License | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #15
Source File: replay_buffer.py From distributional-dqn with MIT License | 5 votes |
def __init__(self, size, alpha): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. alpha: float how much prioritization is used (0 - no prioritization, 1 - full prioritization) See Also -------- ReplayBuffer.__init__ """ super(PrioritizedReplayBuffer, self).__init__(size) assert alpha > 0 self._alpha = alpha it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #16
Source File: replay_buffer.py From emdqn with MIT License | 5 votes |
def __init__(self, size, alpha): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. alpha: float how much prioritization is used (0 - no prioritization, 1 - full prioritization) See Also -------- ReplayBuffer.__init__ """ super(PrioritizedReplayBuffer, self).__init__(size) assert alpha > 0 self._alpha = alpha it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #17
Source File: test_segment_tree.py From emdqn with MIT License | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #18
Source File: replay_buffer.py From BackpropThroughTheVoidRL with MIT License | 5 votes |
def __init__(self, size, alpha): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. alpha: float how much prioritization is used (0 - no prioritization, 1 - full prioritization) See Also -------- ReplayBuffer.__init__ """ super(PrioritizedReplayBuffer, self).__init__(size) assert alpha > 0 self._alpha = alpha it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #19
Source File: test_segment_tree.py From BackpropThroughTheVoidRL with MIT License | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #20
Source File: replay_buffer.py From learning2run with MIT License | 5 votes |
def __init__(self, size, alpha): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. alpha: float how much prioritization is used (0 - no prioritization, 1 - full prioritization) See Also -------- ReplayBuffer.__init__ """ super(PrioritizedReplayBuffer, self).__init__(size) assert alpha > 0 self._alpha = alpha it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #21
Source File: test_segment_tree.py From lirpg with MIT License | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #22
Source File: replay_buffer.py From HardRLWithYoutube with MIT License | 5 votes |
def __init__(self, size, alpha): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. alpha: float how much prioritization is used (0 - no prioritization, 1 - full prioritization) See Also -------- ReplayBuffer.__init__ """ super(PrioritizedReplayBuffer, self).__init__(size) assert alpha >= 0 self._alpha = alpha it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #23
Source File: test_segment_tree.py From HardRLWithYoutube with MIT License | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #24
Source File: test_segment_tree.py From Reinforcement_Learning_for_Traffic_Light_Control with Apache License 2.0 | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #25
Source File: test_segment_tree.py From Reinforcement_Learning_for_Traffic_Light_Control with Apache License 2.0 | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #26
Source File: test_segment_tree.py From Reinforcement_Learning_for_Traffic_Light_Control with Apache License 2.0 | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #27
Source File: replay_buffer.py From rl_graph_generation with BSD 3-Clause "New" or "Revised" License | 5 votes |
def __init__(self, size, alpha): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. alpha: float how much prioritization is used (0 - no prioritization, 1 - full prioritization) See Also -------- ReplayBuffer.__init__ """ super(PrioritizedReplayBuffer, self).__init__(size) assert alpha > 0 self._alpha = alpha it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #28
Source File: test_segment_tree.py From rl_graph_generation with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)
Example #29
Source File: replay_buffer.py From rl-attack-detection with MIT License | 5 votes |
def __init__(self, size, alpha): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. alpha: float how much prioritization is used (0 - no prioritization, 1 - full prioritization) See Also -------- ReplayBuffer.__init__ """ super(PrioritizedReplayBuffer, self).__init__(size) assert alpha > 0 self._alpha = alpha it_capacity = 1 while it_capacity < size: it_capacity *= 2 self._it_sum = SumSegmentTree(it_capacity) self._it_min = MinSegmentTree(it_capacity) self._max_priority = 1.0
Example #30
Source File: test_segment_tree.py From learning2run with MIT License | 5 votes |
def test_max_interval_tree(): tree = MinSegmentTree(4) tree[0] = 1.0 tree[2] = 0.5 tree[3] = 3.0 assert np.isclose(tree.min(), 0.5) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.5) assert np.isclose(tree.min(0, -1), 0.5) assert np.isclose(tree.min(2, 4), 0.5) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 0.7 assert np.isclose(tree.min(), 0.7) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 0.7) assert np.isclose(tree.min(0, -1), 0.7) assert np.isclose(tree.min(2, 4), 0.7) assert np.isclose(tree.min(3, 4), 3.0) tree[2] = 4.0 assert np.isclose(tree.min(), 1.0) assert np.isclose(tree.min(0, 2), 1.0) assert np.isclose(tree.min(0, 3), 1.0) assert np.isclose(tree.min(0, -1), 1.0) assert np.isclose(tree.min(2, 4), 3.0) assert np.isclose(tree.min(2, 3), 4.0) assert np.isclose(tree.min(2, -1), 4.0) assert np.isclose(tree.min(3, 4), 3.0)