Chris Nota

According to our database, Chris Nota authored at least 8 papers between 2019 and 2022.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2022

On the Convergence of Discounted Policy Gradient Methods.

Chris Nota

CoRR, 2022

2021

Posterior Value Functions: Hindsight Baselines for Policy Gradient Methods.

Chris Nota, Philip S. Thomas, Bruno C. da Silva

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Learning Reusable Options for Multi-Task Reinforcement Learning.

Francisco M. Garcia, Chris Nota, Philip S. Thomas

CoRR, 2020

Asynchronous Coagent Networks.

James E. Kostas, Chris Nota, Philip S. Thomas

Proceedings of the 37th International Conference on Machine Learning, 2020

Is the Policy Gradient a Gradient?

Chris Nota, Philip S. Thomas

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

Lifelong Learning with a Changing Action Set.

Yash Chandak, Georgios Theocharous, Chris Nota, Philip S. Thomas

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Classical Policy Gradient: Preserving Bellman's Principle of Optimality.

Philip S. Thomas, Scott M. Jordan, Yash Chandak, Chris Nota, James E. Kostas

CoRR, 2019

Asynchronous Coagent Networks: Stochastic Networks for Reinforcement Learning without Backpropagation or a Clock.

James E. Kostas, Chris Nota, Philip S. Thomas

CoRR, 2019