})
print(f"Environment: {params['env_name']}\n"
f"Number of actions: {params['n_actions']}")
if params["do_intro_env"]:
intro_env()
env = make_atari(params["env_name"], params["seed"])
agent = Agent(**params)
experiment = Experiment()
logger = Logger(agent, experiment=experiment, **params)
if not params["train_from_scratch"]:
chekpoint = logger.load_weights()
agent.online_model.load_state_dict(
chekpoint["online_model_state_dict"])
agent.hard_update_target_network()
params.update({"beta": chekpoint["beta"]})
min_episode = chekpoint["episode"]
print("Keep training from previous run.")
else:
min_episode = 0
print("Train from scratch.")
if params["do_train"]:
sign = lambda x: bool(x > 0) - bool(x < 0)
state = np.zeros(shape=params["state_shape"], dtype=np.uint8)
