mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
240 lines
9.3 KiB
Python
240 lines
9.3 KiB
Python
import numpy as np
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import pickle
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import unittest
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import ray
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from ray.rllib.agents.ppo import PPOTrainer
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from ray.rllib.examples.env.debug_counter_env import DebugCounterEnv
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from ray.rllib.examples.models.rnn_spy_model import RNNSpyModel
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from ray.rllib.models import ModelCatalog
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from ray.rllib.policy.rnn_sequencing import chop_into_sequences
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from ray.rllib.utils.test_utils import check
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from ray.tune.registry import register_env
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class TestLSTMUtils(unittest.TestCase):
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def test_basic(self):
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eps_ids = [1, 1, 1, 5, 5, 5, 5, 5]
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agent_ids = [1, 1, 1, 1, 1, 1, 1, 1]
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f = [[101, 102, 103, 201, 202, 203, 204, 205],
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[[101], [102], [103], [201], [202], [203], [204], [205]]]
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s = [[209, 208, 207, 109, 108, 107, 106, 105]]
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f_pad, s_init, seq_lens = chop_into_sequences(
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episode_ids=eps_ids,
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unroll_ids=np.ones_like(eps_ids),
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agent_indices=agent_ids,
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feature_columns=f,
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state_columns=s,
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max_seq_len=4)
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self.assertEqual([f.tolist() for f in f_pad], [
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[101, 102, 103, 0, 201, 202, 203, 204, 205, 0, 0, 0],
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[[101], [102], [103], [0], [201], [202], [203], [204], [205], [0],
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[0], [0]],
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])
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self.assertEqual([s.tolist() for s in s_init], [[209, 109, 105]])
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self.assertEqual(seq_lens.tolist(), [3, 4, 1])
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def test_multi_dim(self):
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eps_ids = [1, 1, 1]
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agent_ids = [1, 1, 1]
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obs = np.ones((84, 84, 4))
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f = [[obs, obs * 2, obs * 3]]
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s = [[209, 208, 207]]
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f_pad, s_init, seq_lens = chop_into_sequences(
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episode_ids=eps_ids,
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unroll_ids=np.ones_like(eps_ids),
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agent_indices=agent_ids,
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feature_columns=f,
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state_columns=s,
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max_seq_len=4)
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self.assertEqual([f.tolist() for f in f_pad], [
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np.array([obs, obs * 2, obs * 3]).tolist(),
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])
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self.assertEqual([s.tolist() for s in s_init], [[209]])
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self.assertEqual(seq_lens.tolist(), [3])
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def test_batch_id(self):
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eps_ids = [1, 1, 1, 5, 5, 5, 5, 5]
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batch_ids = [1, 1, 2, 2, 3, 3, 4, 4]
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agent_ids = [1, 1, 1, 1, 1, 1, 1, 1]
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f = [[101, 102, 103, 201, 202, 203, 204, 205],
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[[101], [102], [103], [201], [202], [203], [204], [205]]]
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s = [[209, 208, 207, 109, 108, 107, 106, 105]]
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_, _, seq_lens = chop_into_sequences(
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episode_ids=eps_ids,
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unroll_ids=batch_ids,
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agent_indices=agent_ids,
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feature_columns=f,
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state_columns=s,
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max_seq_len=4)
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self.assertEqual(seq_lens.tolist(), [2, 1, 1, 2, 2])
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def test_multi_agent(self):
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eps_ids = [1, 1, 1, 5, 5, 5, 5, 5]
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agent_ids = [1, 1, 2, 1, 1, 2, 2, 3]
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f = [[101, 102, 103, 201, 202, 203, 204, 205],
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[[101], [102], [103], [201], [202], [203], [204], [205]]]
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s = [[209, 208, 207, 109, 108, 107, 106, 105]]
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f_pad, s_init, seq_lens = chop_into_sequences(
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episode_ids=eps_ids,
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unroll_ids=np.ones_like(eps_ids),
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agent_indices=agent_ids,
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feature_columns=f,
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state_columns=s,
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max_seq_len=4,
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dynamic_max=False)
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self.assertEqual(seq_lens.tolist(), [2, 1, 2, 2, 1])
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self.assertEqual(len(f_pad[0]), 20)
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self.assertEqual(len(s_init[0]), 5)
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def test_dynamic_max_len(self):
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eps_ids = [5, 2, 2]
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agent_ids = [2, 2, 2]
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f = [[1, 1, 1]]
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s = [[1, 1, 1]]
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f_pad, s_init, seq_lens = chop_into_sequences(
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episode_ids=eps_ids,
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unroll_ids=np.ones_like(eps_ids),
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agent_indices=agent_ids,
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feature_columns=f,
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state_columns=s,
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max_seq_len=4)
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self.assertEqual([f.tolist() for f in f_pad], [[1, 0, 1, 1]])
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self.assertEqual([s.tolist() for s in s_init], [[1, 1]])
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self.assertEqual(seq_lens.tolist(), [1, 2])
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class TestRNNSequencing(unittest.TestCase):
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def setUp(self) -> None:
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ray.init(num_cpus=4)
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def tearDown(self) -> None:
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ray.shutdown()
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def test_simple_optimizer_sequencing(self):
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ModelCatalog.register_custom_model("rnn", RNNSpyModel)
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register_env("counter", lambda _: DebugCounterEnv())
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ppo = PPOTrainer(
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env="counter",
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config={
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"num_workers": 0,
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"rollout_fragment_length": 10,
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"train_batch_size": 10,
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"sgd_minibatch_size": 10,
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"num_sgd_iter": 1,
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"simple_optimizer": True,
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"model": {
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"custom_model": "rnn",
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"max_seq_len": 4,
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"vf_share_layers": True,
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},
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"framework": "tf",
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})
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ppo.train()
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ppo.train()
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batch0 = pickle.loads(
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ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_0"))
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self.assertEqual(
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batch0["sequences"].tolist(),
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[[[0], [1], [2], [3]], [[4], [5], [6], [7]], [[8], [9], [0], [0]]])
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self.assertEqual(batch0["seq_lens"].tolist(), [4, 4, 2])
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self.assertEqual(batch0["state_in"][0][0].tolist(), [0, 0, 0])
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self.assertEqual(batch0["state_in"][1][0].tolist(), [0, 0, 0])
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self.assertGreater(abs(np.sum(batch0["state_in"][0][1])), 0)
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self.assertGreater(abs(np.sum(batch0["state_in"][1][1])), 0)
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self.assertTrue(
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np.allclose(batch0["state_in"][0].tolist()[1:],
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batch0["state_out"][0].tolist()[:-1]))
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self.assertTrue(
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np.allclose(batch0["state_in"][1].tolist()[1:],
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batch0["state_out"][1].tolist()[:-1]))
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batch1 = pickle.loads(
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ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_1"))
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self.assertEqual(batch1["sequences"].tolist(), [
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[[10], [11], [12], [13]],
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[[14], [0], [0], [0]],
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[[0], [1], [2], [3]],
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[[4], [0], [0], [0]],
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])
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self.assertEqual(batch1["seq_lens"].tolist(), [4, 1, 4, 1])
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self.assertEqual(batch1["state_in"][0][2].tolist(), [0, 0, 0])
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self.assertEqual(batch1["state_in"][1][2].tolist(), [0, 0, 0])
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self.assertGreater(abs(np.sum(batch1["state_in"][0][0])), 0)
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self.assertGreater(abs(np.sum(batch1["state_in"][1][0])), 0)
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self.assertGreater(abs(np.sum(batch1["state_in"][0][1])), 0)
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self.assertGreater(abs(np.sum(batch1["state_in"][1][1])), 0)
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self.assertGreater(abs(np.sum(batch1["state_in"][0][3])), 0)
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self.assertGreater(abs(np.sum(batch1["state_in"][1][3])), 0)
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def test_minibatch_sequencing(self):
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ModelCatalog.register_custom_model("rnn", RNNSpyModel)
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register_env("counter", lambda _: DebugCounterEnv())
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ppo = PPOTrainer(
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env="counter",
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config={
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"shuffle_sequences": False, # for deterministic testing
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"num_workers": 0,
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"rollout_fragment_length": 20,
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"train_batch_size": 20,
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"sgd_minibatch_size": 10,
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"num_sgd_iter": 1,
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"model": {
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"custom_model": "rnn",
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"max_seq_len": 4,
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"vf_share_layers": True,
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},
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"framework": "tf",
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})
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ppo.train()
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ppo.train()
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# first epoch: 20 observations get split into 2 minibatches of 8
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# four observations are discarded
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batch0 = pickle.loads(
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ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_0"))
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batch1 = pickle.loads(
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ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_1"))
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if batch0["sequences"][0][0][0] > batch1["sequences"][0][0][0]:
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batch0, batch1 = batch1, batch0 # sort minibatches
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self.assertEqual(batch0["seq_lens"].tolist(), [4, 4, 2])
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self.assertEqual(batch1["seq_lens"].tolist(), [2, 3, 4, 1])
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check(batch0["sequences"], [
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[[0], [1], [2], [3]],
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[[4], [5], [6], [7]],
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[[8], [9], [0], [0]],
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])
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check(batch1["sequences"], [
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[[10], [11], [0], [0]],
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[[12], [13], [14], [0]],
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[[0], [1], [2], [3]],
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[[4], [0], [0], [0]],
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])
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# second epoch: 20 observations get split into 2 minibatches of 8
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# four observations are discarded
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batch2 = pickle.loads(
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ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_2"))
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batch3 = pickle.loads(
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ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_3"))
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if batch2["sequences"][0][0][0] > batch3["sequences"][0][0][0]:
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batch2, batch3 = batch3, batch2
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self.assertEqual(batch2["seq_lens"].tolist(), [4, 4, 2])
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self.assertEqual(batch3["seq_lens"].tolist(), [4, 4, 2])
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check(batch2["sequences"], [
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[[0], [1], [2], [3]],
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[[4], [5], [6], [7]],
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[[8], [9], [0], [0]],
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])
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check(batch3["sequences"], [
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[[5], [6], [7], [8]],
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[[9], [10], [11], [12]],
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[[13], [14], [0], [0]],
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])
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if __name__ == "__main__":
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import pytest
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import sys
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sys.exit(pytest.main(["-v", __file__]))
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