70aa7bf93e
* Use lower-level API to reduce overhead. * Further improvements. * Buffer _add_to_buffer improvement. * Do not use _data field to store Batch data to avoid overhead. Add back _meta field in Buffer. * Restore metadata attribute to store batch in Buffer. * Move out nested methods. * Update try/catch instead of actual check to efficiency. * Remove unsed branches for efficiency. * Use np.array over list when possible for efficiency. * Final performance improvement. * Add unit tests for Batch size method. * Add missing stack unit tests. * Enforce Buffer initialization to zero. Co-authored-by: Alexis Duburcq <alexis.duburcq@wandercraft.eu>
110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
import numpy as np
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from tianshou.data import Batch, ReplayBuffer, PrioritizedReplayBuffer
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if __name__ == '__main__':
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from env import MyTestEnv
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else: # pytest
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from test.base.env import MyTestEnv
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def test_replaybuffer(size=10, bufsize=20):
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env = MyTestEnv(size)
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buf = ReplayBuffer(bufsize)
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buf2 = ReplayBuffer(bufsize)
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obs = env.reset()
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action_list = [1] * 5 + [0] * 10 + [1] * 10
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for i, a in enumerate(action_list):
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obs_next, rew, done, info = env.step(a)
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buf.add(obs, a, rew, done, obs_next, info)
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obs = obs_next
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assert len(buf) == min(bufsize, i + 1)
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data, indice = buf.sample(bufsize * 2)
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assert (indice < len(buf)).all()
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assert (data.obs < size).all()
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assert (0 <= data.done).all() and (data.done <= 1).all()
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assert len(buf) > len(buf2)
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buf2.update(buf)
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assert len(buf) == len(buf2)
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assert buf2[0].obs == buf[5].obs
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assert buf2[-1].obs == buf[4].obs
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b = ReplayBuffer(size=10)
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b.add(1, 1, 1, 'str', 1, {'a': 3, 'b': {'c': 5.0}})
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assert b.obs[0] == 1
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assert b.done[0] == 'str'
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assert np.all(b.obs[1:] == 0)
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assert np.all(b.done[1:] == np.array(None))
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assert b.info.a[0] == 3 and b.info.a.dtype == np.integer
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assert np.all(b.info.a[1:] == 0)
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assert b.info.b.c[0] == 5.0 and b.info.b.c.dtype == np.inexact
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assert np.all(np.isnan(b.info.b.c[1:]))
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def test_ignore_obs_next(size=10):
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# Issue 82
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buf = ReplayBuffer(size, ignore_obs_net=True)
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for i in range(size):
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buf.add(obs={'mask1': np.array([i, 1, 1, 0, 0]),
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'mask2': np.array([i + 4, 0, 1, 0, 0])},
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act={'act_id': i,
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'position_id': i + 3},
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rew=i,
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done=i % 3 == 0,
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info={'if': i})
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indice = np.arange(len(buf))
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orig = np.arange(len(buf))
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data = buf[indice]
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data2 = buf[indice]
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assert isinstance(data, Batch)
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assert isinstance(data2, Batch)
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assert np.allclose(indice, orig)
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def test_stack(size=5, bufsize=9, stack_num=4):
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env = MyTestEnv(size)
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buf = ReplayBuffer(bufsize, stack_num)
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obs = env.reset(1)
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for i in range(15):
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obs_next, rew, done, info = env.step(1)
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buf.add(obs, 1, rew, done, None, info)
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obs = obs_next
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if done:
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obs = env.reset(1)
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indice = np.arange(len(buf))
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assert np.allclose(buf.get(indice, 'obs'), np.array([
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[1, 1, 1, 2], [1, 1, 2, 3], [1, 2, 3, 4],
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[1, 1, 1, 1], [1, 1, 1, 2], [1, 1, 2, 3],
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[3, 3, 3, 3], [3, 3, 3, 4], [1, 1, 1, 1]]))
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print(buf)
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def test_priortized_replaybuffer(size=32, bufsize=15):
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env = MyTestEnv(size)
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buf = PrioritizedReplayBuffer(bufsize, 0.5, 0.5)
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obs = env.reset()
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action_list = [1] * 5 + [0] * 10 + [1] * 10
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for i, a in enumerate(action_list):
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obs_next, rew, done, info = env.step(a)
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buf.add(obs, a, rew, done, obs_next, info, np.random.randn() - 0.5)
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obs = obs_next
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assert np.isclose(np.sum((buf.weight / buf._weight_sum)[:buf._size]),
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1, rtol=1e-12)
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data, indice = buf.sample(len(buf) // 2)
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if len(buf) // 2 == 0:
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assert len(data) == len(buf)
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else:
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assert len(data) == len(buf) // 2
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assert len(buf) == min(bufsize, i + 1)
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assert np.isclose(buf._weight_sum, (buf.weight).sum())
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data, indice = buf.sample(len(buf) // 2)
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buf.update_weight(indice, -data.weight / 2)
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assert np.isclose(buf.weight[indice], np.power(
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np.abs(-data.weight / 2), buf._alpha)).all()
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assert np.isclose(buf._weight_sum, (buf.weight).sum())
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if __name__ == '__main__':
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test_replaybuffer()
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test_ignore_obs_next()
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test_stack()
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test_priortized_replaybuffer(233333, 200000)
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