I have written a test demo that may help. However, the function def save_or_load_agent(self, cwd: str, if_save: bool) in AgentBase.py has to be modified a little:
def save_or_load_agent(self, cwd: str, if_save: bool):
...
if if_save:
for name, obj in name_obj_list:
save_path = f"{cwd}/{name}.pth"
torch.save(obj.state_dict(), save_path)
else:
for name, obj in name_obj_list:
save_path = f"{cwd}/{name}.pth"
load_torch_file(obj, save_path) if os.path.isfile(save_path) else None
return self.act,self.act_target,self.act_optim,self.cri,self.cri_target, self.cri_optim

import torch
from elegantrl.train.utils import init_agent
from elegantrl.train.config import build_env

import gym
from elegantrl.agents.AgentSAC import AgentSAC, AgentModSAC
from elegantrl.envs.Gym import get_gym_env_args
from elegantrl.train.config import Arguments

get_gym_env_args(gym.make('LunarLanderContinuous-v2'), if_print=True)

env_func = gym.make
env_args = {
'env_num': 1,
'env_name': 'LunarLanderContinuous-v2',
'max_step': 1000,
'state_dim': 8,
'action_dim': 2,
'if_discrete': False,
'target_return': 200,
'id': 'LunarLanderContinuous-v2'
}

args = Arguments(agent=AgentModSAC(), env_func=env_func, env_args=env_args)
args.init_before_training() # necessary!
learner_gpu = args.learner_gpus[0]
env = build_env(env=args.env, env_func=args.env_func, env_args=args.env_args, gpu_id=learner_gpu)
agent = init_agent(args, gpu_id=learner_gpu, env=env)
cwd = args.cwd

act,b,c,d,e,f = agent.save_or_load_agent(cwd, False)

act.load_state_dict(torch.load("actor.pth"))

s=env.reset()
print(s,s.shape)
for i in range(1000):
action=act.get_action(torch.tensor(s))
# agent.train()
next_state,reward,done,_=env.step(action.detach().numpy())
if done:
s=env.reset()
state=next_state
env.render()

Thanks! We are looking into your codes.