调整参数
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@ -1 +1 @@
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python train.py --load_model --batch_size 8 --learning_rate 0.0003 --enable_onnx
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python train.py --load_model --batch_size 4 --learning_rate 0.0003 --enable_onnx
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@ -11,8 +11,8 @@ parser.add_argument('--objective', default='adp', type=str, choices=['adp', 'wp'
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help='Use ADP or WP as reward (default: ADP)')
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help='Use ADP or WP as reward (default: ADP)')
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# Training settings
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# Training settings
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parser.add_argument('--onnx_sync_interval', default=5, type=int,
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parser.add_argument('--onnx_sync_interval', default=30, type=int,
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help='Time interval (in minutes) at which to sync the onnx model')
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help='Time interval (in seconds) at which to sync the onnx model')
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parser.add_argument('--actor_device_cpu', action='store_true',
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parser.add_argument('--actor_device_cpu', action='store_true',
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help='Use CPU as actor device')
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help='Use CPU as actor device')
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parser.add_argument('--gpu_devices', default='0', type=str,
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parser.add_argument('--gpu_devices', default='0', type=str,
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@ -254,7 +254,7 @@ def train(flags):
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checkpoint(frames)
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checkpoint(frames)
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last_checkpoint_time = timer()
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last_checkpoint_time = timer()
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if timer() - last_onnx_sync_time > flags.onnx_sync_interval * 60:
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if timer() - last_onnx_sync_time > flags.onnx_sync_interval:
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sync_onnx_model(frames)
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sync_onnx_model(frames)
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last_onnx_sync_time = timer()
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last_onnx_sync_time = timer()
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