File size: 7,948 Bytes
3e10edb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 |
import itertools
import os
import subprocess
from typing import Any, Dict
from wandb.apis.public import Api
# Configuration - customize these mappings
WANDB_PROJECT = "Arcade-RLC"
WANDB_ENTITY = "bolt-um" # Optional, unless you're in a team
MAX_JOBS = 3 # Maximum number of jobs to run before terminating (useful for HPC/SLURM)
TRAIN_PATH = "/home/smorad/code/popgym_arcade/popgym_arcade/train.py"
algorithm_families = ["PQN"]
models = ["lru", "mingru", "mlp"]
seeds = [0, 1, 2]
environments_config = {
"CartPoleEasy": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"CartPoleMedium": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"CartPoleHard": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"NoisyCartPoleEasy": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"NoisyCartPoleMedium": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"NoisyCartPoleHard": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"BattleShipEasy": {
"PPO": int(2e7), # Different timesteps for PPO
"PQN": int(2e7), # Different timesteps for PQN
"TOTAL_TIMESTEPS_DECAY": int(2e6), # New decay parameter for PQN
},
"BattleShipMedium": {
"PPO": int(2e7),
"PQN": int(2e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"BattleShipHard": {
"PPO": int(2e7),
"PQN": int(2e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"CountRecallEasy": {
"PPO": int(2e7),
"PQN": int(2e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"CountRecallMedium": {
"PPO": int(2e7),
"PQN": int(2e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"CountRecallHard": {
"PPO": int(2e7),
"PQN": int(2e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"NavigatorEasy": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"NavigatorMedium": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"NavigatorHard": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"MineSweeperEasy": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"MineSweeperMedium": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"MineSweeperHard": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"AutoEncodeEasy": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"AutoEncodeMedium": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
"AutoEncodeHard": {
"PPO": int(1e7),
"PQN": int(1e7),
"TOTAL_TIMESTEPS_DECAY": int(2e6),
},
}
partial_flags = [True, False]
def is_rnn(model_str):
return "mlp" not in model_str
def generate_experiment_key(experiment: Dict[str, Any]) -> str:
"""Create a unique key for an experiment configuration"""
return (
f"{experiment['algorithm']}_{experiment['model']}_"
f"{experiment['seed']}_{experiment['environment']}_"
f"{experiment['partial']}"
)
def get_wandb_runs() -> set:
"""Get completed or running experiments from WandB"""
api = Api()
runs = (
api.runs(f"{WANDB_ENTITY}/{WANDB_PROJECT}")
if WANDB_ENTITY
else api.runs(WANDB_PROJECT)
)
existing = set()
for run in runs:
config = {k: v for k, v in run.config.items() if not k.startswith("_")}
key = generate_experiment_key(
{
"algorithm": config["TRAIN_TYPE"].replace("_RNN", ""),
"model": config.get("MEMORY_TYPE", "mlp").lower(),
"seed": config["SEED"],
"environment": config["ENV_NAME"],
"partial": config["PARTIAL"],
}
)
if run.state in ["finished", "running"]:
existing.add(key)
return existing
def build_base_command(experiment: Dict[str, Any]) -> list:
"""Construct the appropriate command based on model type"""
algo = experiment["algorithm"]
algo += "_RNN" if is_rnn(experiment["model"]) else ""
base_cmd = [
"python",
TRAIN_PATH,
algo,
"--PROJECT",
WANDB_PROJECT,
"--SEED",
str(experiment["seed"]),
"--ENV_NAME",
experiment["environment"],
"--TOTAL_TIMESTEPS",
str(experiment["total_timesteps"]),
]
base_cmd += ["--PARTIAL"] if experiment["partial"] else []
if experiment["algorithm"] in ["PQN", "PQN_RNN"]:
base_cmd += [
"--TOTAL_TIMESTEPS_DECAY",
str(experiment["total_timesteps_decay"]),
]
if is_rnn(experiment["model"]):
base_cmd += ["--MEMORY_TYPE", experiment["model"]]
return base_cmd
def get_all_experiments():
"""Return all possible experiments"""
all_experiments = []
for env, config in environments_config.items():
combinations = itertools.product(
seeds, algorithm_families, models, partial_flags
)
for seed, family, model, partial in combinations:
# Get timesteps specific to algorithm family
total_timesteps = config[family] # PPO or PQN
all_experiments.append(
{
"algorithm": family,
"model": model,
"total_timesteps": total_timesteps,
"total_timesteps_decay": config[
"TOTAL_TIMESTEPS_DECAY"
], # Include in config
"seed": seed,
"environment": env,
"partial": partial,
}
)
return all_experiments
def get_pending_experiments(all_experiments):
"""Return experiments that we plan to run"""
# Generate all possible experiment combinations
# Get completed/running experiments from WandB
completed_or_running = get_wandb_runs()
# Find pending experiments
pending_experiments = [
exp
for exp in all_experiments
if generate_experiment_key(exp) not in completed_or_running
]
return completed_or_running, pending_experiments
def main():
all_experiments = get_all_experiments()
# Run experiments sequentially
completed_or_running, pending_experiments = get_pending_experiments(all_experiments)
for i in range(MAX_JOBS):
# for i, experiment in enumerate(pending_experiments):
# print("Currently running or completed experiments:")
# print(completed_or_running)
if not pending_experiments:
print("All experiments have been completed or are running!")
break
# Run experiment
experiment = pending_experiments[0]
pending_experiments = pending_experiments[1:]
print(
f"Found {len(pending_experiments)} pending experiments out of {len(all_experiments)} total"
)
print(f"\n=== Starting experiment {i + 1}/{len(pending_experiments)} ===")
print("Configuration:", experiment)
# Build command
base_cmd = build_base_command(experiment)
# Run experiment
print("Command:", " ".join(base_cmd))
if i + 1 == MAX_JOBS:
print(f"Reached maximum number of jobs ({MAX_JOBS}), terminating")
break
i += 1
if __name__ == "__main__":
main()
|