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View Code? Open in Web Editor NEWPyTorch code for PODNet
PyTorch code for PODNet
Currently, options are represented with a two-dimensional array but one should suffice. For example, change option representation from [[1,0,0],[1,0,0]] to [1,0,0]. Gumbel softmax and everything else still applies.
Add rnn pack_pad_sequences functionality to help PODNet process variable length trajectories in the same minibatch
Parallelize sequence encoder of PODNet with a Transformer style architecture. Create model.
Add position embedding to input states.
Need to post-process the observation vector to extract (x,y) position and agent orientation for the minigrid-gym environment.
Add ability to leverage multiple GPUs for training by having a distributed trainig process (Similar to Hogwild, A3C etc.). Reference. Leveraging DistributedDataParallel for multiple gpus in a single node and multiple processes (each training process on a single node).
Revisit how true_next_state was calculated and inputted in the loss function. Instead of only states, should be something similar to state[i+1] (future state, and not current).
L_BC, L_ODC, Reg = loss_function(
next_state_pred=next_state_pred,
true_next_state=state,
action_pred=action_pred,
true_action=action,
c_t=c_t)
Need a dataloader function for PODNet V2 to load trajectory tensors (in minibatch mode) and pad and split them into segment batches to be fed into the model.
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