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use list with circular indexing instead of deque as the replay buffer

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Nilesh PS 2018-03-28 19:57:26 +05:30
parent 9ac3c27e7b
commit 16e8a8cf61
1 changed files with 17 additions and 0 deletions
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17
16_reinforcement_learning.ipynb
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@ -1576,6 +1576,23 @@
"replay_memory_size = 500000\n", "replay_memory_size = 500000\n",
"replay_memory = deque([], maxlen=replay_memory_size)\n", "replay_memory = deque([], maxlen=replay_memory_size)\n",
"\n", "\n",
"\n",
"class ReplayMemory:\n",
" \n",
" def __init__(self, size):\n",
" self.size = size\n",
" self.buf= [None] * self.size\n",
" self.index = 0\n",
" \n",
" def append(self, data):\n",
" self.buf[self.index] = data\n",
" self.index = (self.index + 1) % self.size\n",
" \n",
" def __getitem__(self, idx):\n",
" return self.buf[idx]\n",
"\n",
" \n",
"\n",
"def sample_memories(batch_size):\n", "def sample_memories(batch_size):\n",
" indices = np.random.permutation(len(replay_memory))[:batch_size]\n", " indices = np.random.permutation(len(replay_memory))[:batch_size]\n",
" cols = [[], [], [], [], []] # state, action, reward, next_state, continue\n", " cols = [[], [], [], [], []] # state, action, reward, next_state, continue\n",