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)

    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()