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View Code? Open in Web Editor NEWOfficial PyTorch Implementation for Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning
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The trained policy weights cannot accomplish the tasks
I used the run_all.sh scripts with "asym_max" critic that trained policies for tasks. However, when I tried loading the pretrained weights, none of the tasks can be accomplished. Here is the function I create for policy loading:
def load_model(self):
#print("esults/HandManipulateBlockRotateZ_her_(-)rew_asym-max_emb16_lr0.001_sd100.pt")
sd = torch.load( os.path.join(self.args.save_dir, f"{self.args.experiment_name}.pt"))
self.actor.load_state_dict(sd["actor"])
if hasattr(self, "critic"):
self.critic.load_state_dict(sd["critic"])
self.actor.eval()
return
and I used the "eval_agent()" for success rate evaluation
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