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Users can now specify and pass wandb details from the config files #48

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b42c4bd
Users can now specify and pass wandb details from the config files
vikashplus May 17, 2023
cc6af69
FEATURE: directory can be specified for wandb logs
vikashplus May 21, 2023
104732f
Users should explicitly import these envs if need be. They have mujoc…
vikashplus Jun 24, 2023
6e05853
Users should explicitly import these envs if need be. They have mujoc…
vikashplus Jun 24, 2023
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6 changes: 6 additions & 0 deletions examples/example_configs/hopper_npg.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -29,5 +29,11 @@

'alg_hyper_params' : dict(),

'wandb_params': {
'use_wandb' : True,
'wandb_user' : 'vikashplus',
'wandb_project' : 'mjrl_demo',
'wandb_exp' : 'demo_exp',
}
}

6 changes: 6 additions & 0 deletions examples/example_configs/swimmer_npg.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -29,4 +29,10 @@

'alg_hyper_params' : dict(),

'wandb_params': {
'use_wandb' : True,
'wandb_user' : 'vikashplus',
'wandb_project' : 'mjrl_demo',
'wandb_exp' : 'demo_exp',
}
}
8 changes: 7 additions & 1 deletion examples/example_configs/swimmer_ppo.txt
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Expand Up @@ -7,7 +7,7 @@
'seed' : 123,
'sample_mode' : 'trajectories',
'rl_num_traj' : 10,
'rl_num_iter' : 50,
'rl_num_iter' : 10,
'num_cpu' : 2,
'save_freq' : 25,
'eval_rollouts' : None,
Expand All @@ -29,4 +29,10 @@

'alg_hyper_params' : dict(clip_coef=0.2, epochs=10, mb_size=64, learn_rate=5e-4),

'wandb_params': {
'use_wandb' : True,
'wandb_user' : 'vikashplus',
'wandb_project' : 'mjrl_demo',
'wandb_exp' : 'demo_exp',
}
}
11 changes: 11 additions & 0 deletions examples/policy_opt_job_script.py
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Expand Up @@ -13,6 +13,7 @@
from mjrl.algos.batch_reinforce import BatchREINFORCE
from mjrl.algos.ppo_clip import PPO
from mjrl.utils.train_agent import train_agent
from mjrl.utils.logger import DataLog
import os
import json
import gym
Expand Down Expand Up @@ -82,6 +83,16 @@
# or defaults in the PPO algorithm will be used
agent = PPO(e, policy, baseline, save_logs=True, **job_data['alg_hyper_params'])


# Update logger if WandB in Config
if 'wandb_params' in job_data.keys() and job_data['wandb_params']['use_wandb']==True:
if 'wandb_logdir' in job_data['wandb_params']:
job_data['wandb_params']['wandb_logdir'] = os.path.join(JOB_DIR, job_data['wandb_params']['wandb_logdir'])
else:
job_data['wandb_params']['wandb_logdir'] = JOB_DIR
agent.logger = DataLog(**job_data['wandb_params'], wandb_config=job_data)


print("========================================")
print("Starting policy learning")
print("========================================")
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3 changes: 2 additions & 1 deletion mjrl/__init__.py
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@@ -1 +1,2 @@
import mjrl.envs
# Users should explicitly import these envs if need be. They have mujoco_py dependency that not all setups have
# import mjrl.envs
10 changes: 8 additions & 2 deletions mjrl/utils/logger.py
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Expand Up @@ -13,14 +13,20 @@

class DataLog:

def __init__(self, use_wandb:bool = True,
def __init__(self,
use_wandb:bool = False,
wandb_user:str = USERNAME,
wandb_project:str = WANDB_PROJECT,
wandb_exp:str = None,
wandb_logdir:str = None,
wandb_config:dict = dict()) -> None:
self.use_wandb = use_wandb
if use_wandb:
import wandb
self.run = wandb.init(project=wandb_project, entity=wandb_user, config=wandb_config)
self.run = wandb.init(project=wandb_project, entity=wandb_user, dir=wandb_logdir, config=wandb_config)
# Update exp name if explicitely specified
if wandb_exp is not None: wandb.run.name = wandb_exp

self.log = {}
self.max_len = 0
self.global_step = 0
Expand Down