22 citations · 85 across the 31 of their papers we have counts for
4 papers · 1 filter
Classical Policy Gradient: Preserving Bellman's Principle of Optimality
Philip S. Thomas, Scott M. Jordan, Yash Chandak +2
We propose a new objective function for finite-horizon episodic Markov decision processes that better captures Bellman's principle of optimality, and provide an expression for the…
Reinforcement Learning When All Actions are Not Always Available
Yash Chandak, Georgios Theocharous, Blossom Metevier +1
The Markov decision process (MDP) formulation used to model many real-world sequential decision making problems does not efficiently capture the setting where the set of available…
Lifelong Learning with a Changing Action Set
Yash Chandak, Georgios Theocharous, Chris Nota +1
In many real-world sequential decision making problems, the number of available actions (decisions) can vary over time. While problems like catastrophic forgetting, changing transi…
Learning Action Representations for Reinforcement Learning
Yash Chandak, Georgios Theocharous, James Kostas +2
Most model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the str…