I need to be able to resume training breakout-v0 after stopping it. I would also like to be able to move a checkpoint dir to another machine and resume training there.
When I train on my laptop, using ubuntu 14.04, I am able to resume after stopping. But on the faster machine I really want to use, I can not resume after stopping. That machine uses ubuntu 16.04, FWIW.
Both machines use tensorflow 1.3.0. The working laptop uses python 3.6 and the non-working machine uses python 3.5.2. OpenAI gym is version 0.9.4 on both machines, as installed by pip. Neither machine uses GPU, and both use NHWC.
On both machines, I have cloned from the devsisters/DQN-tensorflow repository and manually fixed the bugs that prevent it from working with python 3.x.
Martellacci
commented
6 years ago
I need to be able to resume training breakout-v0 after stopping it. I would also like to be able to move a checkpoint dir to another machine and resume training there.
When I train on my laptop, using ubuntu 14.04, I am able to resume after stopping. But on the faster machine I really want to use, I can not resume after stopping. That machine uses ubuntu 16.04, FWIW.
Both machines use tensorflow 1.3.0. The working laptop uses python 3.6 and the non-working machine uses python 3.5.2. OpenAI gym is version 0.9.4 on both machines, as installed by pip. Neither machine uses GPU, and both use NHWC.
On both machines, I have cloned from the devsisters/DQN-tensorflow repository and manually fixed the bugs that prevent it from working with python 3.x.
`~/DQN-tensorflow$ python main.py --env_name=Breakout-v0 --is_train=True --display=False
[*] GPU : 1.0000 {'_save_step': 500000, '_test_step': 50000, 'action_repeat': 4, 'backend': 'tf', 'batch_size': 32, 'cnn_format': 'NHWC', 'discount': 0.99, 'display': False, 'double_q': False, 'dueling': False, 'env_name': 'Breakout-v0', 'env_type': 'detail', 'ep_end': 0.1, 'ep_end_t': 1000000, 'ep_start': 1.0, 'history_length': 4, 'learn_start': 50000.0, 'learning_rate': 0.00025, 'learning_rate_decay': 0.96, 'learning_rate_decay_step': 50000, 'learning_rate_minimum': 0.00025, 'max_delta': 1, 'max_reward': 1.0, 'max_step': 50000000, 'memory_size': 1000000, 'min_delta': -1, 'min_reward': -1.0, 'model': 'm1', 'random_start': 30, 'scale': 10000, 'screen_height': 84, 'screen_width': 84, 'target_q_update_step': 10000, 'train_frequency': 4} WARNING:tensorflow:From /home/mjc/DQN-tensorflow/dqn/agent.py:224: calling argmax (from tensorflow.python.ops.math_ops) with dimension is deprecated and will be removed in a future version. Instructions for updating: Use the
axisargument instead WARNING:tensorflow:From /opt/anaconda/miniconda3/envs/tfbuild/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py:107: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02. Instructions for updating: Usetf.global_variables_initializerinstead.[*] Loading checkpoints... [!] Load FAILED: checkpoints/Breakout-v0/backend-tf/ep_end-0.1/model-m1/screen_width-84/env_type-detail/learning_rate-0.00025/learning_rate_minimum-0.00025/memory_size-1000000/env_name-Breakout-v0/dueling-False/learning_rate_decay-0.96/batch_size-32/min_delta--1/max_reward-1.0/learn_start-50000.0/double_q-False/max_delta-1/scale-10000/random_start-30/cnn_format-NHWC/discount-0.99/min_reward--1.0/action_repeat-4/learning_rate_decay_step-50000/ep_start-1.0/history_length-4/target_q_update_step-10000/ep_end_t-1000000/train_frequency-4/max_step-50000000/screen_height-84/ `
How can this problem be fixed?