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Clear notebook output

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This commit is contained in:
Marek Wolan
2024-01-05 13:11:47 +00:00
parent f75c10aafb
commit d038f63fda
1 changed files with 7 additions and 202 deletions
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209
src/primaite/notebooks/training_example_ray_single_agent.ipynb
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@@ -10,64 +10,9 @@
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"/home/cade/repos/PrimAITE/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"2024-01-05 12:46:28,650\tINFO util.py:159 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n",
"2024-01-05 12:46:31,581\tINFO util.py:159 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n",
"2024-01-05 12:46:31,903\tWARNING __init__.py:10 -- PG has/have been moved to `rllib_contrib` and will no longer be maintained by the RLlib team. You can still use it/them normally inside RLlib util Ray 2.8, but from Ray 2.9 on, all `rllib_contrib` algorithms will no longer be part of the core repo, and will therefore have to be installed separately with pinned dependencies for e.g. ray[rllib] and other packages! See https://github.com/ray-project/ray/tree/master/rllib_contrib#rllib-contrib for more information on the RLlib contrib effort.\n",
"2024-01-05 12:46:35,016\tINFO worker.py:1673 -- Started a local Ray instance.\n"
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" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left\"><b>Ray version:</b></td>\n",
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"source": [
"from primaite.game.game import PrimaiteGame\n",
"import yaml\n",
@@ -96,24 +41,16 @@
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"{'flatten_obs': False}\n"
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"source": [
"print(cfg['agents'][2]['agent_settings'])"
]
},
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@@ -136,141 +73,9 @@
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"Resetting environment, episode 1, avg. reward: -101.0\n",
"Resetting environment, episode 2, avg. reward: -126.5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-05 12:46:46,735\tINFO storage.py:563 -- Checkpoint successfully created at: Checkpoint(filesystem=local, path=/home/cade/ray_results/PPO_2024-01-05_12-46-39/PPO_PrimaiteRayEnv_7899c_00000_0_2024-01-05_12-46-40/checkpoint_000000)\n",
"2024-01-05 12:46:46,847\tINFO tune.py:1047 -- Total run time: 6.85 seconds (6.77 seconds for the tuning loop).\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<IPython.core.display.HTML object>\n"
]
},
{
"data": {
"text/plain": [
"ResultGrid<[\n",
" Result(\n",
" metrics={'custom_metrics': {}, 'episode_media': {}, 'info': {'learner': {'__all__': {'num_agent_steps_trained': 128.0, 'num_env_steps_trained': 128.0, 'total_loss': 9.46448793411255}, 'default_policy': {'total_loss': 9.46448793411255, 'policy_loss': -0.06344481200600664, 'vf_loss': 9.525621096293131, 'vf_loss_unclipped': 509.6841542561849, 'vf_explained_var': 0.004743536313374837, 'entropy': 3.855365761121114, 'mean_kl_loss': 0.011559122211959523, 'default_optimizer_lr': 4.999999999999999e-05, 'curr_lr': 5e-05, 'curr_entropy_coeff': 0.0, 'curr_kl_coeff': 0.20000000298023224}}, 'num_env_steps_sampled': 512, 'num_env_steps_trained': 0, 'num_agent_steps_sampled': 512, 'num_agent_steps_trained': 0}, 'sampler_results': {'episode_reward_max': -101.0, 'episode_reward_min': -126.5, 'episode_reward_mean': -113.75, 'episode_len_mean': 256.0, 'episode_media': {}, 'episodes_this_iter': 1, 'policy_reward_min': {}, 'policy_reward_max': {}, 'policy_reward_mean': {}, 'custom_metrics': {}, 'hist_stats': {'episode_reward': [-101.0, -126.5], 'episode_lengths': [256, 256]}, 'sampler_perf': {'mean_raw_obs_processing_ms': 1.4790121096531605, 'mean_inference_ms': 2.438426005102573, 'mean_action_processing_ms': 0.12985192105746982, 'mean_env_wait_ms': 2.5040151965470656, 'mean_env_render_ms': 0.0}, 'num_faulty_episodes': 0, 'connector_metrics': {'ObsPreprocessorConnector_ms': 1.051938533782959, 'StateBufferConnector_ms': 0.009810924530029297, 'ViewRequirementAgentConnector_ms': 0.46378374099731445}}, 'episode_reward_max': -101.0, 'episode_reward_min': -126.5, 'episode_reward_mean': -113.75, 'episode_len_mean': 256.0, 'episodes_this_iter': 1, 'policy_reward_min': {}, 'policy_reward_max': {}, 'policy_reward_mean': {}, 'hist_stats': {'episode_reward': [-101.0, -126.5], 'episode_lengths': [256, 256]}, 'sampler_perf': {'mean_raw_obs_processing_ms': 1.4790121096531605, 'mean_inference_ms': 2.438426005102573, 'mean_action_processing_ms': 0.12985192105746982, 'mean_env_wait_ms': 2.5040151965470656, 'mean_env_render_ms': 0.0}, 'num_faulty_episodes': 0, 'connector_metrics': {'ObsPreprocessorConnector_ms': 1.051938533782959, 'StateBufferConnector_ms': 0.009810924530029297, 'ViewRequirementAgentConnector_ms': 0.46378374099731445}, 'num_healthy_workers': 0, 'num_in_flight_async_reqs': 0, 'num_remote_worker_restarts': 0, 'num_agent_steps_sampled': 512, 'num_agent_steps_trained': 0, 'num_env_steps_sampled': 512, 'num_env_steps_trained': 0, 'num_env_steps_sampled_this_iter': 128, 'num_env_steps_trained_this_iter': 0, 'num_env_steps_sampled_throughput_per_sec': 57.18780249848288, 'num_env_steps_trained_throughput_per_sec': 0.0, 'num_steps_trained_this_iter': 0, 'agent_timesteps_total': 512, 'timers': {'training_iteration_time_ms': 1392.194, 'sample_time_ms': 995.05, 'synch_weights_time_ms': 1.92}, 'counters': {'num_env_steps_sampled': 512, 'num_env_steps_trained': 0, 'num_agent_steps_sampled': 512, 'num_agent_steps_trained': 0}, 'perf': {'cpu_util_percent': 54.06666666666666, 'ram_util_percent': 53.53333333333334}},\n",
" path='/home/cade/ray_results/PPO_2024-01-05_12-46-39/PPO_PrimaiteRayEnv_7899c_00000_0_2024-01-05_12-46-40',\n",
" filesystem='local',\n",
" checkpoint=Checkpoint(filesystem=local, path=/home/cade/ray_results/PPO_2024-01-05_12-46-39/PPO_PrimaiteRayEnv_7899c_00000_0_2024-01-05_12-46-40/checkpoint_000000)\n",
" )\n",
"]>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"tune.Tuner(\n",
" \"PPO\",\n",