Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information
Authors: Mohit Sharma, Arjun Sharma, Nicholas Rhinehart, Kris M. Kitani
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present results on both discrete and continuous state-action environments. In both of these settings we show that (1) our method is able to segment out sub-tasks from given expert trajectories, (2) learn sub-task conditioned policies, and (3) learn to combine these sub-task policies in order to achieve the task objective. |
| Researcher Affiliation | Academia | Mohit Sharma , Arjun Sharma , Nick Rhinehart, Kris M. Kitani Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213, USA EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | A video of our results on Hopper and Walker environments can be seen at https://sites.google.com/view/directedinfo-gail. |
| Open Datasets | Yes | we also show experiments on Pendulum, Inverted Pendulum, Hopper and Walker environments, provided in Open AI Gym (Brockman et al., 2016). |
| Dataset Splits | No | We used 25 expert trajectories for the Pendulum and Inverted Pendulum tasks and 50 expert trajectories for experiments with the Hopper and Walker environments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | We used Adam (Kingma & Ba, 2014) as our optimizer setting an initial learning rate of 3e 4. Further, we used the Proximal Policy Optimization algorithm (Schulman et al., 2017) to train our policy network with ϵ = 0.2. |
| Experiment Setup | Yes | Table 3 lists the experiment settings for all of the different environments. We use multi-layer perceptrons for our policy (generator), value, reward (discriminator) and posterior function representations. Each network consisted of 2 hidden layers with 64 units in each layer and Re LU as our non-linearity function. We used Adam (Kingma & Ba, 2014) as our optimizer setting an initial learning rate of 3e 4. Further, we used the Proximal Policy Optimization algorithm (Schulman et al., 2017) to train our policy network with ϵ = 0.2. For the VAE pre-training step we set the VAE learning rate also to 3e 4. For the Gumbel-Softmax distribution we set an initial temperature τ = 5.0. The temperature is annealed using using an exponential decay with the following schedule τ = max(0.1, exp kt), where k = 3e 3 and t is the current epoch. |