Benjamin A. Spiegel

Benjamin A. Spiegel

PhD Student @ Brown CS

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

MK-SQuIT: Synthesizing Questions using Iterative Template-filling

Published in arXiv, 2020

The aim of this work is to create a framework for synthetically generating question/query pairs with as little human input as possible. These datasets can be used to train machine translation systems to convert natural language questions into queries, a useful tool that could allow for more natural access to database information. Existing methods of dataset generation require human input that scales linearly with the size of the dataset, resulting in small datasets. Aside from a short initial configuration task, no human input is required during the query generation process of our system. We leverage WikiData, a knowledge base of RDF triples, as a source for generating the main content of questions and queries. Using multiple layers of question templating we are able to sidestep some of the most challenging parts of query generation that have been handled by humans in previous methods; humans never have to modify, aggregate, inspect, annotate, or generate any questions or queries at any step in the process. Our system is easily configurable to multiple domains and can be modified to generate queries in natural languages other than English. We also present an example dataset of 110,000 question/query pairs across four WikiData domains. We then present a baseline model that we train using the dataset which shows promise in a commercial QA setting.

Recommended citation: BA Spiegel, V Cheong, JE Kaplan, A Sanchez. arXiv preprint arXiv:2011.02566 https://arxiv.org/abs/2011.02566

Guided Policy Search for Parameterized Skills using Adverbs

Published in The Interactive Learning with Implicit Human Feedback Workshop at ICML, 2023

We present a method for using adverb phrases to adjust skill parameters via learned adverb-skill groundings. These groundings allow an agent to use adverb feedback provided by a human to directly update a skill policy, in a manner similar to traditional local policy search methods. We show that our method can be used as a drop-in replacement for these policy search methods when dense reward from the environment is not available but human language feedback is. We demonstrate improved sample efficiency over modern policy search methods in two experiments.

Recommended citation: B.A. Spiegel, G.D. Konidaris. In the Interactive Learning with Implicit Human Feedback Workshop at ICML 2023. http://benjaminaspiegel.com/files/Adverbs_ICML_Workshop.pdf

RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents

Published in Proceedings of the Fortieth International Conference on Machine Learning, 2023

We introduce RLang, a domain-specific language (DSL) for communicating domain knowledge to an RL agent. Unlike existing RL DSLs that ground to single elements of a decision-making formalism (e.g., the reward function or policy), RLang can specify information about every element of a Markov decision process. We define precise syntax and grounding semantics for RLang, and provide a parser that grounds RLang programs to an algorithm-agnostic partial world model and policy that can be exploited by an RL agent. We provide a series of example RLang programs demonstrating how different RL methods can exploit the resulting knowledge, encompassing model-free and model-based tabular algorithms, policy gradient and value-based methods, hierarchical approaches, and deep methods.

Recommended citation: R. Rodriguez-Sanchez, B.A. Spiegel, J. Wang, R. Patel, G.D. Konidaris, and S. Tellex. RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents. In Proceedings of the Fortieth International Conference on Machine Learning, July 2023. http://benjaminaspiegel.com/files/RLang_ICML2023__With_Objects_.pdf

Skill Generalization with Verbs

Published in International Conference for Intelligent Robots and Systems (IROS) 2023, 2023

Recommended citation: R. Ma, L. Lam, B.A. Spiegel, A. Ganeshan, R. Patel, B. Abbatematteo, D. Paulius, S. Tellex, G. Konidaris, “Skill Generalization With Verbs” in International Conference for Intelligent Robots and Systems (IROS) 2023, Accepted. https://cs.brown.edu/~gdk/pubs/skillgen_verbs.pdf

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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