def test_descr(self):
params = ParamGrid([('p1', [3.14, 2.71]), ('p2', ['a', 'b', 'c'])])
experiments = [
Experiment('test1', 'python super_rl1.py',
params.generate_params(randomize=False)),
Experiment('test2', 'python super_rl2.py',
params.generate_params(randomize=False)),
]
rd = RunDescription('test_run', experiments)
cmds = rd.generate_experiments()
for command, name, root_dir, env_vars in cmds:
exp_name = split(root_dir)[-1]
self.assertIn('--experiment', command)
self.assertIn('--experiments_root', command)
self.assertTrue(exp_name in name)
self.assertTrue(root_dir.startswith('test_run'))
def test_simple_cmd(self):
logging.disable(logging.INFO)
echo_params = ParamGrid([
('p1', [3.14, 2.71]),
('p2', ['a', 'b', 'c']),
('p3', list(np.arange(3))),
])
experiments = [
Experiment('test_echo1', 'echo',
echo_params.generate_params(randomize=True)),
Experiment('test_echo2', 'echo',
echo_params.generate_params(randomize=False)),
]
train_dir = ensure_dir_exists(join(project_tmp_dir(), 'tests'))
root_dir_name = '__test_run__'
rd = RunDescription(root_dir_name, experiments, train_dir)
args = runner_argparser().parse_args([])
args.max_parallel = 8
args.pause_between = 0
run(rd, args)
logging.disable(logging.NOTSET)
shutil.rmtree(join(train_dir, root_dir_name))
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('seed', [0, 1111, 2222, 3333, 4444, 5555, 6666, 7777, 8888, 9999]),
('env', [
'doom_my_way_home', 'doom_deadly_corridor', 'doom_defend_the_center',
'doom_defend_the_line', 'doom_health_gathering',
'doom_health_gathering_supreme'
]),
])
_experiments = [
Experiment(
'basic_envs_fs4',
'python -m algorithms.appo.train_appo --train_for_env_steps=500000000 --algo=APPO --env_frameskip=4 --use_rnn=True --num_workers=36 --num_envs_per_worker=8 --num_policies=1 --ppo_epochs=1 --rollout=32 --recurrence=32 --batch_size=2048 --wide_aspect_ratio=False',
_params.generate_params(randomize=False),
),
]
RUN_DESCRIPTION = RunDescription('paper_doom_basic_envs_appo_v97_fs4',
experiments=_experiments)
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('ppo_epochs', [1]),
])
_experiments = [
Experiment(
'battle_d4_fs4_pbt',
'python -m algorithms.appo.train_appo --env=doom_battle_d4 --train_for_env_steps=50000000000 --algo=APPO --env_frameskip=4 --use_rnn=True --reward_scale=0.5 --num_workers=24 --num_envs_per_worker=30 --num_policies=4 --ppo_epochs=1 --rollout=32 --recurrence=32 --macro_batch=2048 --batch_size=2048 --wide_aspect_ratio=False --pbt_period_env_steps=5000000',
_params.generate_params(randomize=False),
),
]
RUN_DESCRIPTION = RunDescription('doom_battle_d4_appo_v64_fs4_pbt',
experiments=_experiments,
use_gpus=2,
experiments_per_gpu=-1,
max_parallel=1)
'python -m algorithms.appo.train_appo --env=doom_dwango5_bots_experimental --train_for_seconds=3600000 --algo=APPO --use_rnn=True --gamma=0.995 --env_frameskip=2 --rollout=32 --reward_scale=0.5 --num_workers=18 --num_envs_per_worker=20 --num_policies=1 --ppo_epochs=1 --rollout=32 --recurrence=32 --macro_batch=2048 --batch_size=2048 --res_w=128 --res_h=72 --wide_aspect_ratio=True',
_params.generate_params(randomize=False),
dict(DOOM_DEFAULT_UDP_PORT=35300),
),
Experiment(
'bots_128_fs2_narrow',
'python -m algorithms.appo.train_appo --env=doom_dwango5_bots_experimental --train_for_seconds=3600000 --algo=APPO --use_rnn=True --gamma=0.995 --env_frameskip=2 --rollout=32 --reward_scale=0.5 --num_workers=18 --num_envs_per_worker=20 --num_policies=1 --ppo_epochs=1 --rollout=32 --recurrence=32 --macro_batch=2048 --batch_size=2048 --res_w=128 --res_h=72 --wide_aspect_ratio=False',
_params.generate_params(randomize=False),
dict(DOOM_DEFAULT_UDP_PORT=40300),
),
Experiment(
'bots_128_fs2_wide_adam0.5',
'python -m algorithms.appo.train_appo --env=doom_dwango5_bots_experimental --train_for_seconds=3600000 --algo=APPO --use_rnn=True --gamma=0.995 --env_frameskip=2 --rollout=32 --reward_scale=0.5 --num_workers=18 --num_envs_per_worker=20 --num_policies=1 --ppo_epochs=1 --rollout=32 --recurrence=32 --macro_batch=2048 --batch_size=2048 --res_w=128 --res_h=72 --wide_aspect_ratio=True --adam_beta1=0.5',
_params.generate_params(randomize=False),
dict(DOOM_DEFAULT_UDP_PORT=45300),
),
Experiment(
'bots_128_fs2_narrow_adam0.5',
'python -m algorithms.appo.train_appo --env=doom_dwango5_bots_experimental --train_for_seconds=3600000 --algo=APPO --use_rnn=True --gamma=0.995 --env_frameskip=2 --rollout=32 --reward_scale=0.5 --num_workers=18 --num_envs_per_worker=20 --num_policies=1 --ppo_epochs=1 --rollout=32 --recurrence=32 --macro_batch=2048 --batch_size=2048 --res_w=128 --res_h=72 --wide_aspect_ratio=False --adam_beta1=0.5',
_params.generate_params(randomize=False),
dict(DOOM_DEFAULT_UDP_PORT=50300),
),
]
RUN_DESCRIPTION = RunDescription('doom_bots_v60_sweep',
experiments=_experiments,
pause_between_experiments=120,
use_gpus=4,
experiments_per_gpu=1,
max_parallel=4)
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('seed', [0000, 1111, 2222, 3333]),
])
_experiment = Experiment(
'8_bezier_col_penalties',
'python -m run_algorithm --env=quadrotor_multi --train_for_env_steps=2000000000 --algo=APPO --use_rnn=False --num_workers=36 --num_envs_per_worker=4 --learning_rate=0.0001 --ppo_clip_value=5.0 --recurrence=1 --nonlinearity=tanh --actor_critic_share_weights=False --policy_initialization=xavier_uniform --adaptive_stddev=False --hidden_size=256 --with_vtrace=False --max_policy_lag=100000000 --gae_lambda=1.00 --max_grad_norm=5.0 --exploration_loss_coeff=0.0 --rollout=128 --batch_size=1024 --extend_obs=True --quads_use_numba=True --quads_num_agents=8 --quads_episode_duration=15.0 --quads_mode=ep_rand_bezier --quads_dist_between_goals=0.0 --quads_collision_reward=1.0 --encoder_custom=quad_multi_encoder --with_pbt=False',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('quads_multi_bezier_v112',
experiments=[_experiment])
# On Brain server, when you use num_workers = 72, if the system reports: Resource temporarily unavailable,
# then, try to use two commands below
# export OMP_NUM_THREADS=1
# export USE_SIMPLE_THREADED_LEVEL3=1
# Command to use this script on server:
# xvfb-run python -m runner.run --run=quad_multi_static_goal_3d_sphere_vel --runner=processes --max_parallel=4 --pause_between=1 --experiments_per_gpu=1 --num_gpus=4
# Command to use this script on local machine:
# Please change num_workers to the physical cores of your local machine
# python -m runner.run --run=quad_multi_static_goal_3d_sphere_vel --runner=processes --max_parallel=4 --pause_between=1 --experiments_per_gpu=1 --num_gpus=4
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('seed', [0000, 1111, 2222, 3333]),
])
_experiment = Experiment(
'one_static_goal-mean_embedding-collision_0.1-agents_6',
'python -m run_algorithm --env=quadrotor_multi --train_for_env_steps=1000000000 --algo=APPO --use_rnn=False --num_workers=72 --num_envs_per_worker=4 --learning_rate=0.0001 --adam_eps=1e-8 --ppo_clip_value=5.0 --recurrence=1 --nonlinearity=tanh --actor_critic_share_weights=False --policy_initialization=xavier_uniform --adaptive_stddev=False --hidden_size=64 --with_vtrace=False --max_policy_lag=100000000 --gae_lambda=1.00 --max_grad_norm=0.0 --exploration_loss_coeff=0.0 --rollout=128 --batch_size=1024 --extend_obs=True --quads_use_numba=True --quads_num_agents=6 --quads_episode_duration=7.0 --quads_mode=static_same_goal --quads_formation_size=0.0 --quads_collision_reward=0.1 --encoder_custom=quad_multi_encoder',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('quads_multi_same_goal_v112',
experiments=[_experiment])
# On Brain server, when you use num_workers = 72, if the system reports: Resource temporarily unavailable,
# then, try to use two commands below
# export OMP_NUM_THREADS=1
# export USE_SIMPLE_THREADED_LEVEL3=1
# Command to use this script on server:
# xvfb-run python -m runner.run --run=quad_multi_static_goal --runner=processes --max_parallel=4 --pause_between=1 --experiments_per_gpu=1 --num_gpus=4
# Command to use this script on local machine:
# Please change num_workers to the physical cores of your local machine
# python -m runner.run --run=quad_multi_static_goal --runner=processes --max_parallel=4 --pause_between=1 --experiments_per_gpu=1 --num_gpus=4
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('seed', [0000, 1111, 2222]),
('quads_settle_range_meters', [0.5, 1.0, 1.5]),
('quads_vel_reward_out_range', [1.0, 2.0, 5.0]),
])
_experiment = Experiment(
'8_static_goal-3d_sphere',
'python -m run_algorithm --env=quadrotor_multi --train_for_env_steps=2000000000 --algo=APPO --use_rnn=False --num_workers=36 --num_envs_per_worker=4 --learning_rate=0.0001 --ppo_clip_value=5.0 --recurrence=1 --nonlinearity=tanh --actor_critic_share_weights=False --policy_initialization=xavier_uniform --adaptive_stddev=False --hidden_size=256 --with_vtrace=False --max_policy_lag=100000000 --gae_lambda=1.00 --max_grad_norm=5.0 --exploration_loss_coeff=0.0 --rollout=128 --batch_size=1024 --extend_obs=True --quads_use_numba=True --quads_num_agents=8 --quads_episode_duration=15.0 --quads_mode=static_diff_goal --quads_formation=sphere --quads_formation_size=0.25 --quads_collision_reward=1.0 --quads_settle=True --encoder_custom=quad_multi_encoder --with_pbt=False --experiment=quads_multi_same_goal_gridsearch_v112',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('quads_multi_static_goal_grid_v112', experiments=[_experiment])
# On Brain server, when you use num_workers = 72, if the system reports: Resource temporarily unavailable,
# then, try to use two commands below
# export OMP_NUM_THREADS=1
# export USE_SIMPLE_THREADED_LEVEL3=1
# Command to use this script on server:
# xvfb-run python -m runner.run --run=quad_multi_static_goal_3d_sphere_vel --runner=processes --max_parallel=4 --pause_between=1 --experiments_per_gpu=1 --num_gpus=4
# Command to use this script on local machine:
# Please change num_workers to the physical cores of your local machine
# python -m runner.run --run=quad_multi_static_goal_3d_sphere_vel --runner=processes --max_parallel=4 --pause_between=1 --experiments_per_gpu=1 --num_gpus=4
ACTOR_GPUS = '0' # replace with '0 1 2 3 4 5 6 7' for 8-GPU server
NUM_POLICIES = 1
_basic_cli = f'python -m algorithms.appo.train_appo --train_for_seconds={TIMEOUT_SECONDS} --train_for_env_steps=20000000000 --algo=APPO --gamma=0.997 --use_rnn=True --rnn_num_layers=2 --num_workers={NUM_WORKERS} --num_envs_per_worker=16 --ppo_epochs=1 --rollout=32 --recurrence=32 --batch_size=2048 --num_policies={NUM_POLICIES} --with_pbt=False --max_grad_norm=0.0 --exploration_loss=symmetric_kl --exploration_loss_coeff=0.001 --policy_workers_per_policy=2 --learner_main_loop_num_cores=4 --reward_clip=30'
_params_basic_envs = ParamGrid([
('env', ['doom_benchmark', 'atari_breakout', 'dmlab_benchmark']),
])
_experiment_basic_envs = Experiment(
'benchmark_basic_envs',
_basic_cli,
_params_basic_envs.generate_params(randomize=False),
)
_voxel_cli = f'python -m algorithms.appo.train_appo --train_for_seconds={TIMEOUT_SECONDS} --train_for_env_steps=20000000000 --algo=APPO --gamma=0.997 --use_rnn=True --rnn_num_layers=2 --num_workers={NUM_WORKERS_VOXEL_ENV} --num_envs_per_worker=2 --ppo_epochs=1 --rollout=32 --recurrence=32 --batch_size=2048 --actor_worker_gpus {ACTOR_GPUS} --num_policies={NUM_POLICIES} --with_pbt=False --max_grad_norm=0.0 --exploration_loss=symmetric_kl --exploration_loss_coeff=0.001 --voxel_num_simulation_threads=2 --voxel_use_vulkan=True --policy_workers_per_policy=2 --learner_main_loop_num_cores=4 --reward_clip=30 --voxel_num_envs_per_instance=36 --voxel_num_agents_per_env=1 --pbt_mix_policies_in_one_env=False'
_params_voxel_env = ParamGrid([
('env', ['voxel_env_obstacleshard']),
('voxel_use_vulkan', [True, False]),
])
_experiment_voxel_env = Experiment(
'benchmark_voxel_env',
_voxel_cli,
_params_voxel_env.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription(
'voxel_train_benchmark',
experiments=[_experiment_basic_envs, _experiment_voxel_env])
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('ppo_epochs', [1]),
])
_experiment = Experiment(
'quads_pbt',
'python -m algorithms.appo.train_appo --env=quadrotor_single --train_for_seconds=3600000 --algo=APPO --gamma=0.99 --use_rnn=False --num_workers=72 --num_envs_per_worker=4 --num_policies=8 --ppo_epochs=1 --rollout=128 --recurrence=1 --batch_size=512 --benchmark=False --pbt_replace_reward_gap=0.1 --pbt_replace_reward_gap_absolute=200.0 --pbt_period_env_steps=1000000 --pbt_start_mutation=20000000 --with_pbt=True --adam_eps=1e-8 --nonlinearity=tanh --actor_critic_share_weights=False --policy_initialization=xavier_uniform --adaptive_stddev=False --hidden_size=64 --with_vtrace=False --max_policy_lag=100000000 --gae_lambda=1.00 --max_grad_norm=0.0 --ppo_clip_value=5.0 --entropy_loss_coeff=0.00001 --learning_rate=5e-4',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('quads_single_pbt_v96_v2',
experiments=[_experiment])
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('seed', [1111, 2222, 3333, 4444]),
])
_experiments = [
Experiment(
'battle_fs4',
'python -m algorithms.appo.train_appo --env=doom_battle --train_for_env_steps=4000000000 --algo=APPO --env_frameskip=4 --use_rnn=True --num_workers=72 --num_envs_per_worker=8 --num_policies=1 --ppo_epochs=1 --rollout=32 --recurrence=32 --batch_size=2048 --wide_aspect_ratio=False --max_grad_norm=0.0',
_params.generate_params(randomize=False),
),
]
RUN_DESCRIPTION = RunDescription('paper_doom_battle_appo_v108_fs4', experiments=_experiments)
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('seed', [0000, 1111, 2222, 3333, 4444, 5555, 6666, 7777]),
('batch_size', [256]),
('ppo_epochs', [1]),
('nonlinearity', ['tanh']),
('learning_rate', [1e-4]),
('entropy_loss_coeff', [0.0005]),
('actor_critic_share_weights', ['False']),
('policy_initialization', ['xavier_uniform']),
('max_policy_lag', [50]),
('adaptive_stddev', ['False']),
('initial_stddev', [1.0]),
('hidden_size', [64]),
])
_experiment = Experiment(
'quads_gridsearch',
'python -m run_algorithm --env=quadrotor_single --train_for_env_steps=1000000000 --algo=APPO --gamma=0.99 --use_rnn=False --num_workers=36 --num_envs_per_worker=2 --num_policies=1 --rollout=32 --recurrence=32 --benchmark=False --with_pbt=False --ppo_clip_ratio=0.05',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('quads_single_gridsearch_v89_seeds',
experiments=[_experiment])
from runner.runs.quad_multi_mix_baseline import QUAD_BASELINE_CLI
_params = ParamGrid([
('quads_collision_falloff_radius', [4.0]),
('quads_collision_reward', [5.0]),
('quads_collision_smooth_max_penalty', [10.0]),
('quads_neighbor_encoder_type', ['attention', 'mean_embed']),
('replay_buffer_sample_prob', [0.75]),
('anneal_collision_steps', [0, 200000000]),
])
_experiment = Experiment(
'quad_mix_baseline-8_mixed_replay',
QUAD_BASELINE_CLI,
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('quads_multi_mix_baseline_8a_replay_v2_v115',
experiments=[_experiment])
# On Brain server, when you use num_workers = 72, if the system reports: Resource temporarily unavailable,
# then, try to use two commands below
# export OMP_NUM_THREADS=1
# export USE_SIMPLE_THREADED_LEVEL3=1
# Command to use this script on server:
# xvfb-run python -m runner.run --run=quad_multi_mix_baseline --runner=processes --max_parallel=3 --pause_between=1 --experiments_per_gpu=1 --num_gpus=3
# Command to use this script on local machine:
# Please change num_workers to the physical cores of your local machine
# python -m runner.run --run=quad_multi_mix_baseline --runner=processes --max_parallel=3 --pause_between=1 --experiments_per_gpu=1 --num_gpus=3
('quads_obstacle_num', [1]),
('quads_obstacle_type', ['sphere']),
('quads_obstacle_traj', ['mix']),
('quads_collision_obstacle_reward', [5.0]),
('quads_obstacle_obs_mode', ['absolute']),
('quads_collision_obst_smooth_max_penalty', [10.0]),
('quads_obstacle_hidden_size', [256]),
('replay_buffer_sample_prob', [0.0]),
('quads_obst_penalty_fall_off', [10.0]),
])
_experiment = Experiment(
'quad_mix_baseline_obst_mix-8a',
QUAD_BASELINE_CLI,
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('quads_multi_mix_obst_mix_8a_v115',
experiments=[_experiment])
# On Brain server, when you use num_workers = 72, if the system reports: Resource temporarily unavailable,
# then, try to use two commands below
# export OMP_NUM_THREADS=1
# export USE_SIMPLE_THREADED_LEVEL3=1
# Command to use this script on server:
# xvfb-run python -m runner.run --run=quad_multi_mix_baseline_obstacle_mix --runner=processes --max_parallel=4 --pause_between=1 --experiments_per_gpu=1 --num_gpus=4
# Command to use this script on local machine:
# Please change num_workers to the physical cores of your local machine
# python -m runner.run --run=quad_multi_mix_baseline_obstacle_mix --runner=processes --max_parallel=4 --pause_between=1 --experiments_per_gpu=1 --num_gpus=4
from runner.run_description import RunDescription, Experiment, ParamGrid
NUM_WORKERS_VOXEL_ENV = 48 # typically num logical cores / 2, limited by the num of available Vulkan contexts
TIMEOUT_SECONDS = 180
SAMPLER_GPUS = '0 1 2 3 4 5 6 7' # replace with '0 1 2 3 4 5 6 7' for 8-GPU server
_voxel_env_cli = f'python -m run_algorithm --algo=DUMMY_SAMPLER --num_workers={NUM_WORKERS_VOXEL_ENV} --num_envs_per_worker=1 --experiment=benchmark --sampler_worker_gpus {SAMPLER_GPUS} --voxel_num_envs_per_instance=64 --voxel_num_agents_per_env=2 --voxel_num_simulation_threads=2 --timeout_seconds={TIMEOUT_SECONDS}'
_params_voxel_env = ParamGrid([
('env', [
'voxel_env_TowerBuilding', 'voxel_env_ObstaclesEasy',
'voxel_env_ObstaclesHard', 'voxel_env_Collect', 'voxel_env_Sokoban',
'voxel_env_HexMemory', 'voxel_env_HexExplore', 'voxel_env_Rearrange'
]),
('voxel_use_vulkan', [True]),
])
_experiment_voxel_env = Experiment(
'benchmark_voxel_env_8',
_voxel_env_cli,
_params_voxel_env.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('voxel_bench_sampling_all_envs',
experiments=[_experiment_voxel_env])
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('ppo_epochs', [1]),
])
_experiment = Experiment(
'bots_freedm_fs2',
'python -m algorithms.appo.train_appo --env=doom_freedm --train_for_seconds=360000 --algo=APPO --gamma=0.995 --env_frameskip=2 --use_rnn=True --reward_scale=0.5 --num_workers=20 --num_envs_per_worker=4 --num_policies=1 --ppo_epochs=1 --rollout=32 --recurrence=32 --macro_batch=2048 --batch_size=2048 --benchmark=False --start_bot_difficulty=150',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('doom_freedm_v64_fs2',
experiments=[_experiment],
pause_between_experiments=100,
use_gpus=1,
experiments_per_gpu=-1,
max_parallel=1)
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('seed', [1111, 3333]),
('quads_collision_reward', [0.0, 1.0, 2.0, 5.0]),
])
_experiment = Experiment(
'8_static_goal_sphere',
'python -m run_algorithm --env=quadrotor_multi --train_for_env_steps=2000000000 --algo=APPO --use_rnn=False --num_workers=36 --num_envs_per_worker=4 --learning_rate=0.0001 --ppo_clip_value=5.0 --recurrence=1 --nonlinearity=tanh --actor_critic_share_weights=False --policy_initialization=xavier_uniform --adaptive_stddev=False --hidden_size=256 --with_vtrace=False --max_policy_lag=100000000 --gae_lambda=1.00 --max_grad_norm=5.0 --exploration_loss_coeff=0.0 --rollout=128 --batch_size=1024 --extend_obs=True --quads_use_numba=True --quads_num_agents=8 --quads_episode_duration=15.0 --quads_mode=static_diff_goal --quads_formation_size=0.25 --encoder_custom=quad_multi_encoder --quads_formation=sphere --with_pbt=False',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription(
'quads_multi_static_goal_sphere_8_col_rew_v112', experiments=[_experiment])
# On Brain server, when you use num_workers = 72, if the system reports: Resource temporarily unavailable,
# then, try to use two commands below
# export OMP_NUM_THREADS=1
# export USE_SIMPLE_THREADED_LEVEL3=1
# Command to use this script on server:
# xvfb-run python -m runner.run --run=quad_multi_static_goal_3d_sphere --runner=processes --max_parallel=4 --pause_between=1 --experiments_per_gpu=1 --num_gpus=4
# Command to use this script on local machine:
# Please change num_workers to the physical cores of your local machine
# python -m runner.run --run=quad_multi_static_goal_3d_sphere --runner=processes --max_parallel=4 --pause_between=1 --experiments_per_gpu=1 --num_gpus=4
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('env', [
'voxel_env_rearrange', 'voxel_env_collect', 'voxel_env_obstaclesEasy',
'voxel_env_hexMemory'
]),
('voxel_num_simulation_threads', [1]),
('rnn_num_layers', [2]),
])
_experiment = Experiment(
'voxel_env_pbt',
'python -m algorithms.appo.train_appo --train_for_seconds=360000000 --algo=APPO --gamma=0.997 --use_rnn=True --num_workers=12 --num_envs_per_worker=2 --ppo_epochs=1 --rollout=32 --recurrence=32 --batch_size=2048 --actor_worker_gpus 0 --num_policies=1 --with_pbt=False --max_grad_norm=0.0 --exploration_loss=symmetric_kl --exploration_loss_coeff=0.001 --voxel_num_envs_per_instance=36 --voxel_num_agents_per_env=1 --voxel_num_simulation_threads=1 --voxel_use_vulkan=True --policy_workers_per_policy=2 --learner_main_loop_num_cores=4',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('voxel_env_v114_env_v52',
experiments=[_experiment])
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('ppo_epochs', [1]),
])
_experiments = [
Experiment(
'battle_fs4_100',
'python -m algorithms.appo.train_appo --env=doom_battle --train_for_env_steps=1000000000 --algo=APPO --env_frameskip=4 --use_rnn=True --num_workers=10 --num_envs_per_worker=10 --num_policies=1 --ppo_epochs=1 --rollout=32 --recurrence=32 --macro_batch=2048 --batch_size=2048 --wide_aspect_ratio=False',
_params.generate_params(randomize=False),
),
Experiment(
'battle_fs4_400',
'python -m algorithms.appo.train_appo --env=doom_battle --train_for_env_steps=1000000000 --algo=APPO --env_frameskip=4 --use_rnn=True --num_workers=20 --num_envs_per_worker=20 --num_policies=1 --ppo_epochs=1 --rollout=32 --recurrence=32 --macro_batch=2048 --batch_size=2048 --wide_aspect_ratio=False',
_params.generate_params(randomize=False),
),
Experiment(
'battle_fs4_800',
'algorithms.appo.train_appo --env=doom_battle --train_for_env_steps=1000000000 --algo=APPO --env_frameskip=4 --use_rnn=True --num_workers=20 --num_envs_per_worker=40 --num_policies=1 --ppo_epochs=1 --rollout=32 --recurrence=32 --macro_batch=2048 --batch_size=2048 --wide_aspect_ratio=False',
_params.generate_params(randomize=False),
),
]
RUN_DESCRIPTION = RunDescription('paper_policy_lag_v66_fs4', experiments=_experiments)
from runner.runs.quad_multi_mix_baseline import QUAD_BASELINE_CLI
_params = ParamGrid([
('seed', [0000, 1111, 2222, 3333]),
])
_experiment_no_replay = Experiment(
'quad_mix_baseline-8_mixed_noreplay',
QUAD_BASELINE_CLI + ' --replay_buffer_sample_prob=0.00',
_params.generate_params(randomize=False),
)
_experiment_no_anneal = Experiment(
'quad_mix_baseline-8_mixed_noannealing',
QUAD_BASELINE_CLI + ' --anneal_collision_steps=0',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('paper_quads_multi_mix_baseline_8a_ablation_v115', experiments=[_experiment_no_replay, _experiment_no_anneal])
# On Brain server, when you use num_workers = 72, if the system reports: Resource temporarily unavailable,
# then, try to use two commands below
# export OMP_NUM_THREADS=1
# export USE_SIMPLE_THREADED_LEVEL3=1
# Command to use this script on server:
# xvfb-run python -m runner.run --run=quad_multi_mix_baseline --runner=processes --max_parallel=3 --pause_between=1 --experiments_per_gpu=1 --num_gpus=3
# Command to use this script on local machine:
# Please change num_workers to the physical cores of your local machine
# python -m runner.run --run=quad_multi_mix_baseline --runner=processes --max_parallel=3 --pause_between=1 --experiments_per_gpu=1 --num_gpus=3
_params = ParamGrid([
('seed', [0000, 1111, 2222, 3333]),
('quads_num_agents', [4]),
('quads_episode_duration', [7.0]),
('quads_mode', ['sanity_check']),
('quads_dist_between_goals', [0.0]),
('quads_collision_reward', [0.0]),
])
_experiment = Experiment(
'quads_multi_numba_extend_obs',
'python -m run_algorithm --env=quadrotor_multi --train_for_env_steps=1000000000 --algo=APPO --use_rnn=False --num_workers=72 --num_envs_per_worker=4 --learning_rate=0.0001 --adam_eps=1e-8 --ppo_clip_value=5.0 --recurrence=1 --nonlinearity=tanh --actor_critic_share_weights=False --policy_initialization=xavier_uniform --adaptive_stddev=False --hidden_size=64 --with_vtrace=False --max_policy_lag=100000000 --gae_lambda=1.00 --max_grad_norm=0.0 --exploration_loss_coeff=0.0 --rollout=128 --batch_size=512 --extend_obs=True --quads_use_numba=True',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('quads_multi_sanity_check_v112', experiments=[_experiment])
# Note: Before using this script and running the script, I would recommend you
# reading the spreadsheet, page: sanity_check, first.
# On Brain server, when you use num_workers = 72, if the system reports: Resource temporarily unavailable,
# then, try to use two commands below
# export OMP_NUM_THREADS=1
# export USE_SIMPLE_THREADED_LEVEL3=1
# Command to use this script on server:
# xvfb-run python -m runner.run --run=quad_multi_sanity_check --runner=processes --max_parallel=4 --pause_between=1 --experiments_per_gpu=1 --num_gpus=4
# Command to use this script on local machine:
# Please change num_workers to the physical cores of your local machine
# python -m runner.run --run=quad_multi_sanity_check --runner=processes --max_parallel=4 --pause_between=1 --experiments_per_gpu=1 --num_gpus=4
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('env', [
'MiniGrid-MemoryS7-v0', 'MiniGrid-RedBlueDoors-8x8-v0',
'MiniGrid-MemoryS17Random-v0'
]),
])
_experiment = Experiment(
'mem_minigrid_obs',
'python -m train_pytorch --algo=PPO --rollout=64 --num_envs=96 --recurrence=1 --use_rnn=False --train_for_env_steps=200000000 --prior_loss_coeff=0.005 --obs_mem=True',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('mem_minigrid_obs_v26',
experiments=[_experiment],
pause_between_experiments=5,
use_gpus=2,
experiments_per_gpu=4,
max_parallel=12)
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('seed', [1111, 2222, 3333, 4444, 5555]),
('env', ['doom_health_gathering_supreme']),
])
_experiments = [
Experiment(
'health_0_255',
'python -m algorithms.appo.train_appo --train_for_env_steps=40000000 --algo=APPO --env_frameskip=4 --use_rnn=True --num_workers=20 --num_envs_per_worker=12 --num_policies=1 --ppo_epochs=1 --rollout=32 --recurrence=32 --batch_size=2048 --wide_aspect_ratio=False',
_params.generate_params(randomize=False),
),
Experiment(
'health_128_128',
'python -m algorithms.appo.train_appo --train_for_env_steps=40000000 --algo=APPO --env_frameskip=4 --use_rnn=True --num_workers=20 --num_envs_per_worker=12 --num_policies=1 --ppo_epochs=1 --rollout=32 --recurrence=32 --batch_size=2048 --wide_aspect_ratio=False --obs_subtract_mean=128.0 --obs_scale=128.0',
_params.generate_params(randomize=False),
),
]
RUN_DESCRIPTION = RunDescription('doom_health_gathering_v97_fs4',
experiments=_experiments)
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('env', ['voxel_env_multitask_Obstacles']),
('use_cpc', ['True']),
('seed', [11111, 22222, 33333, 44444, 55555]),
])
_cli = 'python -m algorithms.appo.train_appo --train_for_seconds=360000000 --train_for_env_steps=10000000000 --algo=APPO --gamma=0.997 --use_rnn=True --rnn_num_layers=2 --num_workers=12 --num_envs_per_worker=2 --ppo_epochs=1 --rollout=32 --recurrence=32 --batch_size=2048 --actor_worker_gpus 0 --num_policies=1 --with_pbt=False --max_grad_norm=0.0 --exploration_loss=symmetric_kl --exploration_loss_coeff=0.001 --voxel_num_simulation_threads=1 --voxel_use_vulkan=True --policy_workers_per_policy=2 --learner_main_loop_num_cores=2 --reward_clip=30 --pbt_mix_policies_in_one_env=False'
EXPERIMENT_1AGENT = Experiment(
'voxel_env_multitask_obs',
_cli + ' --voxel_num_envs_per_instance=36 --voxel_num_agents_per_env=1',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('voxel_env_v115_multitask_obstacles_v55',
experiments=[EXPERIMENT_1AGENT])
from runner.run_description import RunDescription, Experiment, ParamGrid
_params_earlystop = ParamGrid([
('seed', [0000, 1111, 2222, 3333, 4444]),
])
_experiment_earlystop = Experiment(
'lunar_lander_cont',
'python -m examples.train_gym_env --train_for_env_steps=500000000 --algo=APPO --num_workers=20 --num_envs_per_worker=6 --seed 0 --gae_lambda 0.99 --experiment=lunar_lander_2 --env=gym_LunarLanderContinuous-v2 --exploration_loss_coeff=0.0 --max_grad_norm=0.0 --encoder_type=mlp --encoder_subtype=mlp_mujoco --encoder_extra_fc_layers=0 --hidden_size=128 --policy_initialization=xavier_uniform --actor_critic_share_weights=False --adaptive_stddev=False --recurrence=1 --use_rnn=False --batch_size=256 --ppo_epochs=4 --with_vtrace=False --reward_scale=0.05 --max_policy_lag=100000 --save_every_sec=15 --experiment_summaries_interval=10',
_params_earlystop.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('lunar_lander_cont_v100', experiments=[_experiment_earlystop])
from runner.run_description import RunDescription
from runner.runs.voxel_base_experiments import EXPERIMENT_1AGENT
RUN_DESCRIPTION = RunDescription('voxel_env_v115_single_v55',
experiments=[EXPERIMENT_1AGENT])
('ppo_clip_value', [10]),
('with_vtrace', ['False']),
('learning_rate', [0.0001]),
('max_grad_norm', [100.0]),
('use_rnn', ['False']),
('recurrence', [1]),
('num_minibatches_to_accumulate', [0]),
('device', ['gpu']),
('actor_critic_share_weights', ['False']),
('max_policy_lag', [1000000]),
('adaptive_stddev', ['False']),
('ppo_epochs', [20]),
('ppo_clip_ratio', [0.3]),
('batch_size', [1024]),
('num_batches_per_iteration', [10]),
('rollout', [128]),
('nonlinearity', ['tanh']),
('exploration_loss_coeff', [0.0]),
])
_experiment = Experiment(
'mujoco_hopper',
'python -m run_algorithm --env=mujoco_hopper --train_for_env_steps=7000000 --algo=APPO --num_workers=16 --num_envs_per_worker=4 --benchmark=False --with_pbt=False',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('mujoco_hopper_v94', experiments=[_experiment])
# python -m runner.run --run=mujoco_halfcheetah_grid_search --runner=processes --max_parallel=8 --pause_between=1 --experiments_per_gpu=10000 --num_gpus=1
from runner.run_description import RunDescription
from runner.runs.voxel_base_experiments import EXPERIMENT_2AGENTS, EXPERIMENT_4AGENTS
RUN_DESCRIPTION = RunDescription(
'voxel_env_v115_multi_agent_v55',
experiments=[EXPERIMENT_2AGENTS, EXPERIMENT_4AGENTS])
from runner.run_description import RunDescription, Experiment, ParamGrid
_params = ParamGrid([
('ppo_epochs', [1]),
])
_experiment = Experiment(
'voxel_env_pbt',
'python -m algorithms.appo.train_appo --env=voxel_env_v23_v --train_for_seconds=360000000 --algo=APPO --gamma=0.997 --use_rnn=True --num_workers=28 --num_envs_per_worker=2 --ppo_epochs=1 --rollout=32 --recurrence=32 --batch_size=2048 --actor_worker_gpus 0 1 2 3 4 5 6 7 --num_policies=8 --with_pbt=True --max_grad_norm=0.0 --pbt_replace_reward_gap_absolute=0.3 --exploration_loss=symmetric_kl --exploration_loss_coeff=0.001 --pbt_mix_policies_in_one_env=False --experiment=voxel_env_v23_v --voxel_num_envs_per_instance=48 --voxel_num_agents_per_env=4 --voxel_num_simulation_threads=4 --voxel_vertical_look_limit=0.2 --voxel_use_vulkan=True --policy_workers_per_policy=2 --learner_main_loop_num_cores=4',
_params.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('voxel_env_pbt_v112_env_v23_8p',
experiments=[_experiment])
from runner.run_description import RunDescription, Experiment, ParamGrid
_params_earlystop = ParamGrid([
('batch_size', [140]),
('learning_rate', [0.001, 0.0004, 0.0002, 0.0001, 0.00005]),
('ppo_epochs', [4, 8]),
('num_batches_per_iteration', [50]),
('exploration_loss_coeff', [0.0]),
('quads_effort_reward', [0.05]),
])
_experiment_earlystop = Experiment(
'quads_gridsearch_earlystop',
'python -m run_algorithm --env=quadrotor_single --train_for_env_steps=1000000000 --algo=APPO --gamma=0.99 --use_rnn=False --num_workers=24 --num_envs_per_worker=2 --num_policies=1 --rollout=700 --recurrence=1 --benchmark=False --with_pbt=False --ppo_clip_ratio=0.05 --batch_size=128 --nonlinearity=tanh --actor_critic_share_weights=False --policy_initialization=xavier_uniform --adaptive_stddev=False --hidden_size=256 --with_vtrace=False --max_policy_lag=100000000 --gae_lambda=1.00 --device=cpu --max_grad_norm=0.0 --num_minibatches_to_accumulate=0',
_params_earlystop.generate_params(randomize=False),
)
RUN_DESCRIPTION = RunDescription('quads_single_gridsearch_v93_h256',
experiments=[_experiment_earlystop])
