Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
Authors: Kei Ota, Tomoaki Oiki, Devesh Jha, Toshisada Mariyama, Daniel Nikovski
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through numerical experiments, we show that the proposed method outperforms several other state-of-the-art algorithms in terms of both sample efficiency and performance. In this section, we try to answer the following questions with our experiments to describe the performance of OFENet. |
| Researcher Affiliation | Industry | 1Mitsubishi Electric Corporation, Kanagawa, Japan 2Mitsubishi Electric Research Laboratory, Cambridge, USA. |
| Pseudocode | Yes | Algorithm 1 outlines this procedure. The psuedo-code for the proposed method is presented in Algorithm 1. |
| Open Source Code | Yes | Codes for the proposed method are available at http://www.merl.com/research/license/OFENet. |
| Open Datasets | Yes | All these experiments are done in Mu Jo Co simulation environment. Figure 4 shows the learning curves on Open AI Gym tasks. |
| Dataset Splits | Yes | To measure the auxiliary score, we collect 100K transitions as a training set and 20K transitions as a test set, using a random policy. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like Mu Jo Co, Open AI Gym, and the Adam optimizer, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The SAC agent is trained with the hyper-parameters described in (Haarnoja et al., 2018), where the networks have two hidden layers which have 256 units. All the networks are trained with mini-batches of size 256 and Adam optimizer, with a learning rate 3 10 4. |