def build_and_train(slot_affinity_code, log_dir, run_ID, config_key):
affinity = get_affinity(slot_affinity_code)
config = configs[config_key]
# variant = load_variant(log_dir)
# config = update_config(config, variant)
sampler = CpuParallelSampler(
EnvCls=AtariEnv,
env_kwargs=config["env"],
CollectorCls=EpisodicLivesWaitResetCollector,
TrajInfoCls=AtariTrajInfo,
**config["sampler"]
)
algo = A2C(optim_kwargs=config["optim"], **config["algo"])
agent = AtariFfAgent(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): # Might have to flatten 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)
config["eval_env"]["game"] = config["env"]["game"]
CollectorCls = config["sampler"].pop("CollectorCls", None)
sampler = CpuParallelSampler(
EnvCls=AtariEnv,
env_kwargs=config["env"],
CollectorCls=CollectorCls or WaitResetCollector,
TrajInfoCls=AtariTrajInfo,
eval_env_kwargs=config["eval_env"],
**config["sampler"]
)
algo = DQN(optim_kwargs=config["optim"], **config["algo"])
agent = AtariDqnAgent(model_kwargs=config["model"], **config["agent"])
runner = MinibatchRlEval(
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, 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 = CpuParallelSampler(EnvCls=gym_make,
env_kwargs=config["env"],
CollectorCls=ResetCollector,
**config["sampler"])
algo = DDPG(optim_kwargs=config["optim"], **config["algo"])
agent = DdpgAgent(**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 = CpuParallelSampler(
EnvCls=AtariEnv,
env_kwargs=config["env"],
CollectorCls=EpisodicLivesWaitResetCollector,
**config["sampler"]
)
algo = A2C(optim_kwargs=config["optim"], **config["algo"])
agent = AtariLstmAgent(model_kwargs=config["model"], **config["agent"])
runner = MinibatchRl(
algo=algo,
agent=agent,
sampler=sampler,
affinity=affinity,
**config["runner"]
)
name = config["env"]["game"] + str(config["algo"]["entropy_loss_coeff"])
with logger_context(log_dir, run_ID, name, config):
runner.train()
