def main(cfg: DictConfig):
# print all configs
print(cfg.pretty())
# build env
print("===INITIALIZING ENV===")
env = build_env(cfg.experiment_info)
print(env.reset())
print("=================")
# build model
print("===INITIALIZING MODEL===")
cfg.model.params.model_cfg.state_dim = env.observation_space.shape
cfg.model.params.model_cfg.action_dim = env.action_space.n
cfg.model.params.model_cfg.fc.output.params.output_size = env.action_space.n
model = build_model(cfg.model)
test_input = torch.FloatTensor(env.reset()).unsqueeze(0)
print(model)
print(model.forward(test_input))
print("===================")
# build action_selector
print("===INITIALIZING ACTION SELECTOR===")
action_selector = build_action_selector(cfg.experiment_info)
print(action_selector)
print("==============================")
# build loss
print("===INITIALIZING LOSS===")
loss = build_loss(cfg.experiment_info)
print(loss)
print("==================")
# build learner
print("===INITIALIZING LEARNER===")
learner = build_learner(**cfg)
print(learner)
print("=====================")
# build agent
print("===INITIALIZING AGENT===")
agent = build_agent(**cfg)
print(agent)
print("=====================")
def main(cfg: DictConfig):
agent = build_agent(**cfg)
agent.train()