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 [1].
Learning Actionable Representations with Goal Conditioned Policies
Authors: Dibya Ghosh, Abhishek Gupta, Sergey Levine
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on a number of simulated environments, and compare it to prior methods for representation learning, exploration, and hierarchical reinforcement learning. |
| Researcher Affiliation | Academia | Dibya Ghosh, Abhishek Gupta, & Sergey Levine Department of Electrical Engineering and Computer Science University of California, Berkeley Berkeley, CA 94703, USA |
| Pseudocode | No | No pseudocode or algorithm blocks are present. |
| Open Source Code | No | No information about open-source code availability is provided. |
| Open Datasets | No | We study six simulated environments as illustrated in Figure 4: 2D navigation tasks in two settings, wheeled locomotion tasks in two settings, legged locomotion, and object pushing with a robotic gripper. |
| Dataset Splits | Yes | holding out 20% of the trajectories as a validation set. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models) are provided. "computational resources from Amazon" is too vague. |
| Software Dependencies | No | The paper mentions algorithms and optimizers (TRPO, Adam) but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The mean, ยตฮธ( , ) is a fully-connected neural network which takes in the state and the desired goal state as a concatenated vector, and has three hidden layers containing 150, 100, and 50 units respectively. ฮฃ is a learned diagonal covariance matrix, and is initially set to ฮฃ = I. |