help="rank of attention matrix (default: 5)")
args = parser.parse_args()
# Set seed for all randomness sources
utils.seed(args.seed)
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Device: {device}\n")
# Load environment
env = utils.make_env(args.env, args.seed)
for _ in range(args.shift):
env.reset()
print("Environment loaded\n")
# Load agent
model_dir = utils.get_model_dir(args.model)
agent = utils.Agent(env.observation_space,
env.action_space,
model_dir,
device=device,
argmax=args.argmax,
use_memory=args.memory,
use_text=args.text,
hca_returns=args.hcareturns,
wandb_dir = wandb.run.dir if args.wandb is not None else None
# Set seed for all randomness sources
utils.seed(args.seed)
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
txt_logger.info(f"Device: {device}\n")
# Load environments
envs = []
for i in range(args.procs):
envs.append(utils.make_env(args.env, args.seed + 10000 * i))
txt_logger.info("Environments loaded\n")
# Load training status
try:
status = utils.get_status(model_dir)
except OSError:
status = {"num_frames": 0, "update": 0}
txt_logger.info("Training status loaded\n")
# Load observations preprocessor
if args.algo in ("attention"):
from rl_credit.algos.attention import get_obss_preprocessor
obs_space, preprocess_obss = get_obss_preprocessor(
args = parser.parse_args()
# Set seed for all randomness sources
utils.seed(args.seed)
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Device: {device}\n")
# Load environments
envs = []
for i in range(args.procs):
env = utils.make_env(args.env, args.seed + 10000 * i)
envs.append(env)
env = ParallelEnv(envs)
print("Environments loaded\n")
# Load agent
model_dir = utils.get_model_dir(args.model)
agent = utils.Agent(env.observation_space, env.action_space, model_dir,
device=device, argmax=args.argmax, num_envs=args.procs,
use_memory=args.memory, use_text=args.text)
print("Agent loaded\n")
# Initialize logs
logs = {"num_frames_per_episode": [], "return_per_episode": []}