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Found 5 papers, 0 papers with code
CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations
Optimal Power Flow (OPF) refers to a wide range of related optimization problems with the goal of operating power systems efficiently and securely.
Disentangled Predictive Representation for Meta-Reinforcement Learning
A major challenge in reinforcement learning is the design of agents that are able to generalize across tasks that share common dynamics.
Expected Eligibility Traces
The question of how to determine which states and actions are responsible for a certain outcome is known as the credit assignment problem and remains a central research question in reinforcement learning and artificial intelligence.
State2vec: Off-Policy Successor Features Approximators
In this paper, we propose state2vec, an efficient and low-complexity framework for learning successor features which (i) generalize across policies, (ii) ensure sample-efficiency during meta-test.
Representation Learning on Graphs: A Reinforcement Learning Application
In this work, we study value function approximation in reinforcement learning (RL) problems with high dimensional state or action spaces via a generalized version of representation policy iteration (RPI).
