nikhilbarhate99/hierarchical-actor-critic-hac-pytorch

PyTorch implementation of Hierarchical Actor Critic (HAC) for OpenAI gym environments

hierarchical-actor-critic-hac-pytorch's Issues

that HINDSIGHT GOAL TRANSITIONS described in the paper used the state as the action in the transition but i did not see in the code

or did i miss some details?

action and state offsets?

I am just curious what do the action/state offset values mean?

https://github.com/nikhilbarhate99/Hierarchical-Actor-Critic-HAC-PyTorch/blob/master/train.py#L36

I can't seem to figure it out. How do you determine them, for example, for a new environment?

Similarly, for the clip low/high values for both action and states? If you could explain those as well I would appreciate it.

Thank you.

DDPG implementation does not use Ornstein-Uhlenbeck process for time-correlated exploration?

Hindsight Experience Replay .

Reg. Getting Gradient Loss values while testing

Hi I want to get the gradient loss with respect to input while I am testing. I tried the following in HAC.py after the next_state, rew, done, _ = env.step(action) line in the low level policy:

state = torch.FloatTensor(state).to(device)
action = torch.FloatTensor(action).to(device)
goal = torch.FloatTensor(goal).to(device)
criticval = self.HAC[0].critic(state, action, goal).detach()

with plans of computing a new target_Q in the moment to calculate loss and then gradient. However, I get the error Dimension out of range (expected to be in range of [-1, 0], but got 1). Any reason why this is happening?

Goal for pendulum?