def build_and_train(
slot_affinity_code="0slt_1gpu_1cpu",
log_dir="test",
run_ID="0",
config_key="ppo_16env",
experiment_title="exp",
snapshot_mode="none",
snapshot_gap=None,
):
affinity = affinity_from_code(slot_affinity_code)
config = configs[config_key]
variant = load_variant(log_dir)
config = update_config(config, variant)
# Hack that the first part of the log_dir matches the source of the model
model_base_dir = config["pretrain"]["model_dir"]
if model_base_dir is not None:
raw_log_dir = log_dir.split(experiment_title)[-1].lstrip(
"/") # get rid of ~/GitRepos/adam/rlpyt/data/local/<timestamp>/
model_sub_dir = raw_log_dir.split("/RlFromUl/")[
0] # keep the UL part, which comes first
config["agent"]["state_dict_filename"] = osp.join(
model_base_dir, model_sub_dir, "run_0/params.pkl")
pprint.pprint(config)
sampler = AlternatingSampler(
EnvCls=DmlabEnv,
env_kwargs=config["env"],
CollectorCls=GpuWaitResetCollector,
# TrajInfoCls=AtariTrajInfo,
# eval_env_kwargs=config["env"], # Same args!
**config["sampler"])
algo = PPO(optim_kwargs=config["optim"], **config["algo"])
agent = DmlabPgLstmAlternatingAgent(model_kwargs=config["model"],
**config["agent"])
runner = MinibatchRl(algo=algo,
agent=agent,
sampler=sampler,
affinity=affinity,
**config["runner"])
name = config["env"]["level"]
if snapshot_gap is not None:
snapshot_gap = int(snapshot_gap)
with logger_context(
log_dir,
run_ID,
name,
config,
snapshot_mode=snapshot_mode,
snapshot_gap=snapshot_gap,
):
runner.train()
def build_and_train(slot_affinity_code, log_dir, run_ID, config_key):
affinity = affinity_from_code(slot_affinity_code)
config = configs[config_key]
variant = load_variant(log_dir)
config = update_config(config, variant)
sampler = AlternatingSampler(EnvCls=gym_make,
env_kwargs=config["env"],
**config["sampler"])
algo = A2C(optim_kwargs=config["optim"], **config["algo"])
agent = MujocoFfAgent(model_kwargs=config["model"], **config["agent"])
runner = MinibatchRl(algo=algo,
agent=agent,
sampler=sampler,
affinity=affinity,
**config["runner"])
name = config["env"]["id"]
with logger_context(log_dir, run_ID, name, config):
runner.train()
def build_and_train(slot_affinity_code, log_dir, run_ID, config_key):
affinity = affinity_from_code(slot_affinity_code)
config = configs[config_key]
variant = load_variant(log_dir)
config = update_config(config, variant)
sampler = AlternatingSampler(EnvCls=ProcgenEnv,
env_kwargs=config["env"],
CollectorCls=GpuResetCollector,
**config["sampler"])
algo = PPO(optim_kwargs=config["optim"], **config["algo"])
agent = ProcgenFfAgent(model_kwargs=config["model"], **config["agent"])
runner = MinibatchRl(algo=algo,
agent=agent,
sampler=sampler,
affinity=affinity,
**config["runner"])
name = config["env"]["game"]
with logger_context(log_dir, run_ID, name, config):
runner.train()
def build_and_train(
slot_affinity_code="0slt_1gpu_1cpu",
log_dir="test",
run_ID="0",
config_key="ppo_ul_16env",
snapshot_mode="none",
snapshot_gap=None,
):
affinity = affinity_from_code(slot_affinity_code)
config = configs[config_key]
variant = load_variant(log_dir)
config = update_config(config, variant)
pprint.pprint(config)
sampler = AlternatingSampler(
EnvCls=DmlabEnv,
env_kwargs=config["env"],
CollectorCls=GpuWaitResetCollector,
**config["sampler"]
)
algo = PpoUl(optim_kwargs=config["optim"], **config["algo"])
agent = DmlabPgLstmAlternatingAgent(model_kwargs=config["model"], **config["agent"])
runner = MinibatchRl(
algo=algo, agent=agent, sampler=sampler, affinity=affinity, **config["runner"]
)
name = config["env"]["level"]
if snapshot_gap is not None:
snapshot_gap = int(snapshot_gap)
with logger_context(
log_dir,
run_ID,
name,
config,
snapshot_mode=snapshot_mode,
snapshot_gap=snapshot_gap,
):
runner.train()