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Found 19 papers, 11 papers with code
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments
In this work, we consider the off-policy policy evaluation problem for contextual bandits and finite horizon reinforcement learning in the nonstationary setting.
Optimization using Parallel Gradient Evaluations on Multiple Parameters
We propose a first-order method for convex optimization, where instead of being restricted to the gradient from a single parameter, gradients from multiple parameters can be used during each step of gradient descent.
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments
Methods for sequential decision-making are often built upon a foundational assumption that the underlying decision process is stationary.
Understanding Self-Predictive Learning for Reinforcement Learning
We identify that a faster paced optimization of the predictor and semi-gradient updates on the representation, are crucial to preventing the representation collapse.
On Optimizing Interventions in Shared Autonomy
Shared autonomy refers to approaches for enabling an autonomous agent to collaborate with a human with the aim of improving human performance.
SOPE: Spectrum of Off-Policy Estimators
In this paper, we present a new perspective on this bias-variance trade-off and show the existence of a spectrum of estimators whose endpoints are SIS and IS.
Universal Off-Policy Evaluation
When faced with sequential decision-making problems, it is often useful to be able to predict what would happen if decisions were made using a new policy.
High-Confidence Off-Policy (or Counterfactual) Variance Estimation
Many sequential decision-making systems leverage data collected using prior policies to propose a new policy.
Towards Safe Policy Improvement for Non-Stationary MDPs
Many real-world sequential decision-making problems involve critical systems with financial risks and human-life risks.
Reinforcement Learning for Strategic Recommendations
Strategic recommendations (SR) refer to the problem where an intelligent agent observes the sequential behaviors and activities of users and decides when and how to interact with them to optimize some long-term objectives, both for the user and the business.
Evaluating the Performance of Reinforcement Learning Algorithms
Performance evaluations are critical for quantifying algorithmic advances in reinforcement learning.
Optimizing for the Future in Non-Stationary MDPs
Most reinforcement learning methods are based upon the key assumption that the transition dynamics and reward functions are fixed, that is, the underlying Markov decision process is stationary.
Classical Policy Gradient: Preserving Bellman's Principle of Optimality
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 gradient of the objective.
Reinforcement Learning When All Actions are Not Always Available
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 decisions (actions) at each time step is stochastic.
Lifelong Learning with a Changing Action Set
have been well-studied in the lifelong learning literature, the setting where the action set changes remains unaddressed.
Learning Action Representations for Reinforcement Learning
Most model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the structure is provided a priori.
HOPF: Higher Order Propagation Framework for Deep Collective Classification
Given a graph where every node has certain attributes associated with it and some nodes have labels associated with them, Collective Classification (CC) is the task of assigning labels to every unlabeled node using information from the node as well as its neighbors.
Fusion Graph Convolutional Networks
State-of-the-art models for node classification on such attributed graphs use differentiable recursive functions that enable aggregation and filtering of neighborhood information from multiple hops.
On Optimizing Human-Machine Task Assignments
When crowdsourcing systems are used in combination with machine inference systems in the real world, they benefit the most when the machine system is deeply integrated with the crowd workers.
