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ddpg_with_dynamics_pendulum
Run-Skeleton-Run/trying_ddpg_with_implicit_dynamics.ipynb
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wassname adcafeada3 add debug, vs multithread
2018-01-19 12:08:49 +08:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2018-01-19T02:03:04.176331Z",
"start_time": "2018-01-19T02:03:04.170597Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"os.environ['CUDA_VISIBLE_DEVICES']=\"\"\n",
"os.environ[\"PYTHONPATH\"]='.'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2018-01-19T02:03:04.586375Z",
"start_time": "2018-01-19T02:03:04.178204Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab --no-import-all inline\n",
"%reload_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2018-01-19T02:03:04.627472Z",
"start_time": "2018-01-19T02:03:04.587944Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"['ddpg/train.py',\n",
" '--logdir',\n",
" './outputs/logs_ddpg2',\n",
" '--num-threads',\n",
" '3',\n",
" '--reward-scale',\n",
" '0.001',\n",
" '--actor-layers',\n",
" '64-64-64',\n",
" '--actor-layer-norm',\n",
" '--actor-parameters-noise',\n",
" '--actor-lr',\n",
" '0.001',\n",
" '--actor-lr-end',\n",
" '0.000001',\n",
" '--dynamics-lr',\n",
" '0.0001',\n",
" '--dynamics-lr-end',\n",
" '0.0000001',\n",
" '--critic-layers',\n",
" '64-32',\n",
" '--critic-layer-norm',\n",
" '--critic-lr',\n",
" '0.002',\n",
" '--critic-lr-end',\n",
" '0.000001',\n",
" '--initial-epsilon',\n",
" '0.5',\n",
" '--final-epsilon',\n",
" '0.001',\n",
" '--tau',\n",
" '0.0001']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.sys.argv=\"ddpg/train.py --logdir ./outputs/logs_ddpg2 \\\n",
"--num-threads 3 --num-train-threads 3 \\\n",
"--reward-scale 0.001 \\\n",
"--actor-layers 64-64-64 --actor-layer-norm --actor-parameters-noise --actor-lr 0.001 --actor-lr-end 0.000001 \\\n",
"--dynamics-lr 0.0001 --dynamics-lr-end 0.0000001 \\\n",
"--critic-layers 64-32 --critic-layer-norm --critic-lr 0.002 --critic-lr-end 0.000001 \\\n",
"--initial-epsilon 0.5 --final-epsilon 0.001 --tau 0.0001\".split(\" \")\n",
"os.sys.argv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"start_time": "2018-01-19T02:03:03.852Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"from ddpg.train import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"start_time": "2018-01-19T02:03:03.855Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[2018-01-19 10:03:05,421] Making new env: Pendulum-v0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Base (\n",
" (feature_net): LinearNet (\n",
" (net): Sequential (\n",
" (linear_0): NoisyLinear (3 -> 64)\n",
" (layer_norm_0): LayerNorm (\n",
" )\n",
" (act_0): ReLU ()\n",
" (linear_1): NoisyLinear (64 -> 64)\n",
" (layer_norm_1): LayerNorm (\n",
" )\n",
" (act_1): ReLU ()\n",
" (linear_2): NoisyLinear (64 -> 64)\n",
" (layer_norm_2): LayerNorm (\n",
" )\n",
" (act_2): ReLU ()\n",
" )\n",
" )\n",
")\n",
"ActorHead (\n",
" (base): Base (\n",
" (feature_net): LinearNet (\n",
" (net): Sequential (\n",
" (linear_0): NoisyLinear (3 -> 64)\n",
" (layer_norm_0): LayerNorm (\n",
" )\n",
" (act_0): ReLU ()\n",
" (linear_1): NoisyLinear (64 -> 64)\n",
" (layer_norm_1): LayerNorm (\n",
" )\n",
" (act_1): ReLU ()\n",
" (linear_2): NoisyLinear (64 -> 64)\n",
" (layer_norm_2): LayerNorm (\n",
" )\n",
" (act_2): ReLU ()\n",
" )\n",
" )\n",
" )\n",
" (policy_net): LinearNet (\n",
" (net): Sequential (\n",
" (linear_0): Linear (64 -> 1)\n",
" (act_0): ELU (alpha=1.0)\n",
" )\n",
" )\n",
")\n",
"CriticHead (\n",
" (base): Base (\n",
" (feature_net): LinearNet (\n",
" (net): Sequential (\n",
" (linear_0): NoisyLinear (3 -> 64)\n",
" (layer_norm_0): LayerNorm (\n",
" )\n",
" (act_0): ReLU ()\n",
" (linear_1): NoisyLinear (64 -> 64)\n",
" (layer_norm_1): LayerNorm (\n",
" )\n",
" (act_1): ReLU ()\n",
" (linear_2): NoisyLinear (64 -> 64)\n",
" (layer_norm_2): LayerNorm (\n",
" )\n",
" (act_2): ReLU ()\n",
" )\n",
" )\n",
" )\n",
" (value_net): Linear (64 -> 1)\n",
")\n",
"DynamicsHead (\n",
" (base): Base (\n",
" (feature_net): LinearNet (\n",
" (net): Sequential (\n",
" (linear_0): NoisyLinear (3 -> 64)\n",
" (layer_norm_0): LayerNorm (\n",
" )\n",
" (act_0): ReLU ()\n",
" (linear_1): NoisyLinear (64 -> 64)\n",
" (layer_norm_1): LayerNorm (\n",
" )\n",
" (act_1): ReLU ()\n",
" (linear_2): NoisyLinear (64 -> 64)\n",
" (layer_norm_2): LayerNorm (\n",
" )\n",
" (act_2): ReLU ()\n",
" )\n",
" )\n",
" )\n",
" (value_net): LinearNet (\n",
" (net): Sequential (\n",
" (linear_0): Linear (65 -> 64)\n",
" (layer_norm_0): LayerNorm (\n",
" )\n",
" (act_0): ReLU ()\n",
" )\n",
" )\n",
" (value_net2): Linear (64 -> 3)\n",
")\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[2018-01-19 10:03:05,570] Making new env: Pendulum-v0\n",
"[2018-01-19 10:03:05,589] Making new env: Pendulum-v0\n",
"Process Process-1:\n",
"Traceback (most recent call last):\n",
" File \"/home/isisilon/.pyenv/versions/3.6.0/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
" self.run()\n",
" File \"/home/isisilon/.pyenv/versions/3.6.0/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
" self._target(*self._args, **self._kwargs)\n",
" File \"/media/isisilon/Data/My_Documents/Documents/eclipse-workspace/ddpg_implicit_state/Run-Skeleton-Run/ddpg/model.py\", line 440, in train_single_thread\n",
" logger.scalar_summary(key, value, update_step)\n",
" File \"/media/isisilon/Data/My_Documents/Documents/eclipse-workspace/ddpg_implicit_state/Run-Skeleton-Run/common/logger.py\", line 17, in scalar_summary\n",
" self.writer.add_scalar(tag, value, step)\n",
" File \"/home/isisilon/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/tensorboardX/writer.py\", line 262, in add_scalar\n",
" self.file_writer.add_summary(scalar(tag, scalar_value), global_step)\n",
" File \"/home/isisilon/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/tensorboardX/summary.py\", line 88, in scalar\n",
" assert(scalar.squeeze().ndim==0), 'scalar should be 0D'\n",
"AttributeError: 'NoneType' object has no attribute 'squeeze'\n",
"[2018-01-19 10:04:01,980] Making new env: Pendulum-v0\n",
"[2018-01-19 10:04:05,897] Making new env: Pendulum-v0\n",
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"[2018-01-19 10:38:35,036] Making new env: Pendulum-v0\n"
]
}
],
"source": [
"os.environ['OMP_NUM_THREADS'] = '3'\n",
"torch.set_num_threads(1)\n",
"args = parse_args()\n",
"train(args,\n",
" create_model,\n",
" create_act_update_fns,\n",
" train_multi_thread,\n",
" train_single_thread,\n",
" play_single_thread)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2018-01-19T02:01:33.149379Z",
"start_time": "2018-01-19T02:01:30.923966Z"
}
},
"outputs": [],
"source": []
},
{
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"ExecuteTime": {
"start_time": "2018-01-18T08:39:00.929Z"
},
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},
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"source": []
},
{
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"metadata": {
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},
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}
],
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