I'm running your code and loading the 'planner_path: "ltl/craft_new_best_0708.pt' file didn't exist. SO we swtiched to use planner_path: "ltl/craft_new_best_0708.pt". It's getting the following issues
Traceback (most recent call last):
File "./reinforce.py", line 900, in <module>
train(model, adam_optim,
File "./reinforce.py", line 611, in train
loss = run_epoch(train_iter,
File "./reinforce.py", line 399, in run_epoch
loss = forward_pass(batch, model,
File "./reinforce.py", line 361, in forward_pass
rewards = compute_ltl_rewards(mr_words, mr_lengths,
File "/home/arc/grounded_LTL_parser/rewards.py", line 424, in compute_ltl_rewards
agent.actor_critic.load_state_dict(torch.load(model_path, map_location=ltl_args.device)[0])#TODO location
File "/home/arc/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1671, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for LTLActorCritic:
Unexpected key(s) in state_dict: "base.boundary.obs_linear.weight", "base.boundary.obs_linear.bias", "base.boundary.rnn.weight_ih_l0", "base.boundary.rnn.weight_hh_l0", "base.boundary.rnn.bias_ih_l0", "base.boundary.rnn.bias_hh_l0", "base.boundary.rnn.weight_ih_l1", "base.boundary.rnn.weight_hh_l1", "base.boundary.rnn.bias_ih_l1", "base.boundary.rnn.bias_hh_l1", "base.boundary.out_linear.weight", "base.boundary.out_linear.bias".
size mismatch for base.tree.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).
size mismatch for base.workbench.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).
size mismatch for base.factory.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).
size mismatch for base.iron.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).
size mismatch for base.gold.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).
size mismatch for base.gem.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).
size mismatch for base.C.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).
size mismatch for base.&.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).
size mismatch for base.|.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).
size mismatch for base.G.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).
size mismatch for base.F.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).
size mismatch for base.X.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).
size mismatch for base.U.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).
size mismatch for base.!.obs_linear.weight: copying a param with shape torch.Size([64, 393]) from checkpoint, the shape in current model is torch.Size([64, 367]).