single-shot-robot

Yonigoz's group workspace

Group: soft_modularization

1-4

of 4

Tags

Notes

Author

josselin

State

Finished

Start time

September 12th, 2023 11:41:05 PM

Runtime

6h 27m 19s

Tracked hours

6h 27m 8s

Run path

single-shot-robot/PTSL_MTRL10-1M/vrg5m5qw

Linux-5.15.0-83-generic-x86_64-with-glibc2.10

Python version

3.8.0

Git repository

git clone https://github.com/JosselinSomervilleRoberts/mtrl.git

Git state

git checkout -b "soft_modularization_4" 7e73fac2445b5f1683708bd1af08864df0ed036d

Command

main.py setup=metaworld env=metaworld-mt10 agent=state_sac experiment.num_eval_episodes=10 experiment.num_train_steps=1000000 setup.seed_ref=4 setup.num_seeds=1 setup.name=soft_modularization replay_buffer.batch_size=1280 agent.multitask.num_envs=10 agent.multitask.should_use_disentangled_alpha=True agent.multitask.should_use_task_encoder=True agent.encoder.type_to_select=feedforward agent.multitask.actor_cfg.should_condition_model_on_task_info=True agent.multitask.actor_cfg.should_condition_encoder_on_task_info=False agent.multitask.actor_cfg.should_concatenate_task_info_with_encoder=False agent.multitask.actor_cfg.moe_cfg.should_use=True agent.multitask.actor_cfg.moe_cfg.mode=soft_modularization agent.multitask.should_use_multi_head_policy=False agent.encoder.feedforward.hidden_dim=50 agent.encoder.feedforward.num_layers=2 agent.encoder.feedforward.feature_dim=50 agent.actor.num_layers=4 agent.multitask.task_encoder_cfg.model_cfg.pretrained_embedding_cfg.should_use=False

System Hardware

CPU count	8
Logical CPU count	16
GPU count	1
GPU type	NVIDIA RTX A3000 Laptop GPU

W&B CLI Version

0.15.3

Group

soft_modularization

Config parameters are your model's inputs. Learn more

▶
Config parameters:{} 21 keys
- actor/hidden_dim:
  326
- actor/num_layer:
  4
- agent:
  "state_sac"
- agent/encoder_feature_dim:
  50
- agent/num_filters:
  0
- agent/num_layers:
  0
- batch_size:
  1,280
- encoder:
  "feedforward"
- eval_freq:
  10,000
- lr/actor:
  0.0003
- lr/alpha:
  0.0003
- lr/critic:
  0.0003
- lr/decoder:
  0.0003
- lr/encoder:
  0.0003
- num_eval_episodes:
  10
- num_tasks:
  10
- num_train_steps:
  1,000,000
- pal/dim:
  50
- pal/residual:
  "none"
- pal/shared:
  true
- seed:
  4

Summary metrics are your model's outputs. Learn more

▶
Summary metrics:{} 32 keys
- Env 0/episode_reward_env_index_:
  440.1932351373732
- Env 0/success_env_index_:
  1
- Env 1/episode_reward_env_index_:
  36.027061002322235
- Env 1/success_env_index_:
  0
- Env 2/episode_reward_env_index_:
  110.54853532757272
- Env 2/success_env_index_:
  1
- Env 3/episode_reward_env_index_:
  121.76105927154674
- Env 3/success_env_index_:
  1
- Env 4/episode_reward_env_index_:
  404.6097370475445
- Env 4/success_env_index_:
  0
- Env 5/episode_reward_env_index_:
  216.55236969287807
- Env 5/success_env_index_:
  1
- Env 6/episode_reward_env_index_:
  273.97295509137024
- Env 6/success_env_index_:
  1
- Env 7/episode_reward_env_index_:
  -494.4854936028486
- Env 7/success_env_index_:
  0
- Env 8/episode_reward_env_index_:
  134.21072518662524
- Env 8/success_env_index_:
  0
- Env 9/episode_reward_env_index_:
  176.57723082796625
- Env 9/success_env_index_:
  0
- Eval/episode:
  6,600
- Eval/episode_reward:
  154.24970312494958
- Eval/success:
  0.7
- Train/actor_entropy:
  2.8348600562413533
- Train/actor_loss:
  -90.81153452555338
- Train/actor_target_entropy:
  -4
- Train/alpha_loss:
  0.0005754406338868041
- Train/batch_reward:
  1.0098210859298706
- Train/critic_loss:
  46.412356783548994
- Train/duration:
  3.4763238430023193
- Train/episode:
  6,666
- Train/success:
  0.5

This run produced these artifacts as outputs. Total: 1. Learn more

wandb-history

run-vrg5m5qw-history:v0