'sgd_minibatch_size': 100 if large else 64,
'train_batch_size': 10000 if large else 2048,
"num_gpus": 0.25,
"num_cpus_per_worker": args.num_cpus_per_worker,
"num_cpus_for_driver": args.num_cpus_for_driver,
"num_envs_per_worker": 8 if large else 10,
'num_workers': 8 if large else 1,
"callbacks": {"on_train_result": None}
}
if env_name == "FetchPush-v1":
stop = int(3e6)
config.update(
num_workers=8,
num_envs_per_worker=10,
gamma=0.95,
lr=5e-4,
delay_update=not args.no_delay_update
)
train(
"PPO",
config=config,
stop=stop,
exp_name=exp_name,
num_seeds=args.num_seeds,
num_gpus=args.num_gpus,
test_mode=args.test,
keep_checkpoints_num=10
)
stop = int(5e7)
config = {
"num_sgd_iter": 10,
"num_envs_per_worker": 1,
"entropy_coeff": 0.001,
"lambda": 0.95,
"lr": 2.5e-4,
# 'sample_batch_size': 200 if large else 50,
# 'sgd_minibatch_size': 100 if large else 64,
# 'train_batch_size': 10000 if large else 2048,
"num_gpus": 1,
"num_cpus_per_worker": 1,
"num_cpus_for_driver": 2,
'num_workers': 16
}
config.update(
get_marl_env_config(env_name, tune.grid_search([args.num_agents])))
train(
DiCETrainer,
config=config,
stop=stop,
exp_name=exp_name,
num_seeds=args.num_seeds,
num_gpus=args.num_gpus,
test_mode=args.test,
)
1000,
"sample_batch_size":
1,
"train_batch_size":
256,
"target_network_update_freq":
1,
"timesteps_per_iteration":
1000,
"learning_starts":
10000,
"clip_actions":
False,
"normalize_actions":
True,
"evaluation_interval":
1,
"metrics_smoothing_episodes":
5,
"num_cpus_for_driver":
2,
}
train("SAC",
exp_name=exp_name,
keep_checkpoints_num=5,
stop=stop,
config=sac_config,
num_gpus=args.num_gpus,
num_seeds=args.num_seeds)
"num_sgd_iter": 10,
"lr": tune.grid_search([0.0001, 5e-5]),
'rollout_fragment_length': 50,
'sgd_minibatch_size': 64,
'train_batch_size': 2048,
"num_gpus": 0.2,
"num_envs_per_worker": 5,
'num_workers': 7,
"num_cpus_per_worker": 0.5,
"num_cpus_for_driver": 0.5,
constants.USE_BISECTOR: tune.grid_search([False]),
constants.ONLY_TNB: tune.grid_search([True, False]),
# "vf_ratio_clip_param": tune.grid_search([0.05]),
}
config.update(
get_marl_env_config(env_name, tune.grid_search([args.num_agents]))
)
train(
DiCETrainer,
config=config,
stop=stop,
exp_name=exp_name,
num_seeds=args.num_seeds,
num_gpus=args.num_gpus,
test_mode=args.test,
keep_checkpoints_num=5
)
config["model"]["custom_options"].pop("std_mode")
if algo in ["ES", "ARS"]:
config["num_gpus"] = 0
config["num_cpus_per_worker"] = 0.5
config["num_workers"] = 15
config["num_cpus_for_driver"] = 0.5
# # test
# config["model"]["custom_options"]["num_components"] = 2
# initialize_ray(test_mode=True, local_mode=True)
# trainer = GaussianESTrainer(config=config, env="BipedalWalker-v2")
if args.redis_password:
from toolbox import initialize_ray
import os
initialize_ray(address=os.environ["ip_head"],
test_mode=args.test,
redis_password=args.redis_password)
train(get_dynamic_trainer(algo),
config=config,
stop=stop,
exp_name=exp_name,
num_seeds=args.num_seeds,
num_gpus=args.num_gpus,
test_mode=test,
keep_checkpoints_num=5,
start_seed=args.start_seed)
"kl_coeff": 1.0,
"num_sgd_iter": 10,
"lr": 0.0001,
'rollout_fragment_length': 200 if large else 50,
'sgd_minibatch_size': 100 if large else 64,
'train_batch_size': 10000 if large else 2048,
"num_gpus": 0.25,
"num_cpus_per_worker": args.num_cpus_per_worker,
"num_cpus_for_driver": args.num_cpus_for_driver,
"num_envs_per_worker": 8 if large else 10,
'num_workers': 8 if large else 1,
"callbacks": {
"on_train_result": None
}
}
if args.ppo:
config["env"] = env_name
else:
config.update(
get_marl_env_config(env_name, tune.grid_search([args.num_agents])))
train("PPO" if args.ppo else DiCETrainer,
config=config,
stop=stop,
exp_name=exp_name,
num_seeds=args.num_seeds,
num_gpus=args.num_gpus,
test_mode=args.test,
keep_checkpoints_num=3)
8,
"entropy_coeff":
0.001,
"lambda":
0.95,
"lr":
2.5e-4,
"num_gpus":
1, # Force to run 2 concurrently
"evolution": {
"train_batch_size": 4000, # The same as PPO
"num_workers": 10, # default is 10,
"num_cpus_per_worker": 0.5,
"optimizer_type": "adam" if args.adam_optimizer else "sgd"
},
# locomotion config
"kl_coeff":
1.0,
}
train(EPTrainer,
config=config,
stop=1e7,
exp_name=exp_name,
num_seeds=args.num_seeds,
test_mode=args.test,
checkpoint_freq=args.checkpoint_freq,
start_seed=args.start_seed,
verbose=1)
