<p>Not unless you do some "smoothing" like in <a href="https://arxiv.org/abs/1603.08983" rel="nofollow noreferrer">Adaptive Computation Time</a>. FYI, ACT can be tricky to use and train. I have witnessed several projects trying it out. It did not provide great benefit compared to tuned number of steps in their setting. One important thing about ACT (and likely other similar approaches) is that it averages RNN states are different time steps, which essentially means that it assumes that the network learns a "linear representation".</p>
Can the timesteps T in deep reinforcement learning be trained in tensorflow?
<p>Recently I'm trying to implement a deep reinforcement learning project which require a variable timesteps.I want to train a network to output a parameter T,and use T as the length or timesteps of policy gradient method or DQN method,I wonder if that's implementable? I mean when we do back-propogate, can me back-propogate through timesteps T？</p>