Sample Efficient Actor-Critic with Experience Replay (1611.01224v2)

Abstract: This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems. To achieve this, the paper introduces several innovations, including truncated importance sampling with bias correction, stochastic dueling network architectures, and a new trust region policy optimization method.

• The paper presents a novel actor-critic algorithm that integrates experience replay to significantly reduce sample complexity.
• It details effective off-policy corrections that enable stable learning while leveraging past experiences for continuous policy improvement.
• Experimental results demonstrate superior performance on benchmark tasks compared to traditional actor-critic methods.

Overview of "Formatting instructions for NIPS 2016"

The paper "Formatting instructions for NIPS 2016" by David S. Hippocampus provides detailed LaTeX formatting guidelines for authors intending to submit manuscripts to the Neural Information Processing Systems (NIPS) conference in 2016. As an indispensable resource, the document outlines critical formatting standards to ensure consistency, readability, and ease of review.

Key Formatting Guidelines

The paper delineates specifics across multiple sections:

1. Submission Requirements: Authors must adhere to an overall page limit of eight pages for content, with a possible additional ninth page reserved exclusively for references and acknowledgments. Non-compliance results in rejection without review.
2. Style Files: NIPS 2016 utilizes a dedicated LaTeX style file (nips_2016.sty). The paper emphasizes using the current style file and discourages any manual tweaking, which is grounds for rejection.
3. General Formatting: The guidelines stipulate text confined within a 5.5 x 9-inch rectangle, with 1.5-inch left margins. The document encourages Times New Roman 10-point type with an 11-point leading and prescribes precise title formatting with specific font sizes and spacing. Three levels of headings are defined with exact specifications for each.
4. Submission Instructions: The mechanism for electronic submissions is detailed, featuring anonymization settings and line number requirements to aid the review process. The URL for submission is also provided for direct access.
5. Figures and Tables: Authors are instructed to prepare clean, legible figures and tables. Figures must have captions following specific formatting rules, while tables require using the booktabs package for high-quality typesetting and avoiding vertical rules.
6. Citations and References: The natbib package is mandated for citation management. Authors must maintain consistency in citation style (author/year or numeric) and are admonished to avoid first-person references to anonymize their submissions.
7. PDF Preparation: Compliance with Type 1 or Embedded TrueType fonts in PDF submissions is crucial. The document outlines filename conventions and font settings to avoid rendering issues.

Numerical Results and Claims

While the paper is primarily instructional and does not contain experimental results, its significant contribution lies in standardizing manuscript presentation to facilitate the peer-review process—implicitly enhancing the quality of submissions.

Practical and Theoretical Implications

On a practical level, the detailed formatting instructions afford a unified look across submissions, minimizing reviewer distractions and fostering equitability in the assessment process. The guidelines lay a robust foundation for authors, alleviating common submission errors and ensuring focus on substantive content rather than technical presentation.

Theoretically, such standardization enhances the robustness of academic communication within the machine learning community. It underscores the importance of reproducibility, ease of knowledge dissemination, and the critical role of meticulous documentation standards in advancing the field.

Future Developments in AI

Looking ahead, the evolution of formatting standards may integrate automated compliance checks and more advanced tools to streamline manuscript preparation. Enhanced digital submission platforms could offer real-time feedback on adherence to guidelines, reducing the administrative overhead for authors and reviewers alike. Moreover, as AI continues to advance, incorporating intelligent document formatting assistants leveraging natural language processing could become prevalent, simplifying adherence to complex submission requirements.

In conclusion, "Formatting instructions for NIPS 2016" serves as an essential resource for authors aiming to submit high-quality, standardized manuscripts to the NIPS conference. By providing clear, detailed guidelines, the document ensures a consistent, professional presentation of research, fostering an equitable and efficient peer-review process.