On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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7 results for “Reward-Agnostic”

Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learningcs.LG

New algorithm finds optimal policies without knowing reward functions.

problem Reward-agnostic exploration in reinforcement learning.

method Designs an algorithm that explores without reward information, achieving minimax optimality.

result Achieves provable minimax optimality in finding optimal policies for multiple reward functions.

reinforcement learning exploration reward-agnostic minimax offline RL

2023-04-14abs ↗pdf ↗

Provable Reward-Agnostic Preference-Based Reinforcement Learningcs.LG

Proposes a new theoretical framework for PbRL that requires less human feedback.

problem Lack of theoretical work capturing practical PbRL frameworks.

method Introduces a reward-agnostic PbRL framework that acquires exploratory trajectories before human feedback.

result Demonstrates improved sample complexity for learning optimal policies in linear and low-rank MDPs.

Preference-based Reinforcement Learning Reward-Agnostic Theoretical Analysis Human Feedback Sample Complexity

2023-05-29abs ↗pdf ↗

Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learningcs.LG

Paper proposes a hybrid RL algorithm that combines offline and online data without needing reward info.

problem How to efficiently use online data to improve RL policies using only offline data.

method A three-stage hybrid RL algorithm that uses reward-agnostic exploration and model-based offline RL.

result The hybrid RL algorithm outperforms both pure offline and pure online RL in sample complexity.

hybrid reinforcement learning reward-agnostic offline RL online RL sample complexity

2023-05-17abs ↗pdf ↗

Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexitycs.LG

This paper optimizes model-based RL for two-player zero-sum games with near-optimal sample complexity.

problem Optimizing model-based reinforcement learning for two-player zero-sum games with minimal samples.

method Model-based reinforcement learning approach for two-player discounted zero-sum Markov games with a generative model.

result Achieves a sample complexity of

ilde O(|S||A||B|(1-γ)^{-3}ε^{-2})

for finding the Nash equilibrium and ε-NE policies.

reinforcement learning multi-agent systems zero-sum games sample complexity Nash equilibrium

2020-07-15abs ↗pdf ↗

Provably Efficient Reward-Agnostic Navigation with Linear Value Iterationcs.LG

New algorithm learns optimal policies without explicit rewards.

problem Learning optimal policies without explicit rewards.

method Developed a computationally tractable algorithm for reward-free navigation using linear value iteration.

result PAC guarantees on learning near optimal value functions and policies.

reward-free navigation linear value iteration PAC guarantees optimal policies

2020-08-18abs ↗pdf ↗

Low-Rank MDPs with Continuous Action Spacescs.LG

New method extends low-rank MDPs to continuous action spaces.

problem Limited applicability of current low-rank MDP methods to continuous action spaces.

method Extending FLAMBE algorithm to continuous action spaces with Hölder smoothness conditions.

result Similar PAC bound achieved for continuous actions with polynomial dependence on smoothness order.

reinforcement learning Markov Decision Processes continuous actions Hölder smoothness PAC bounds

2023-11-06abs ↗pdf ↗

Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPsstat.ML

Hybrid RL algorithms improve offline and online RL in linear MDPs.

problem Improving RL performance without single-policy concentrability.

method Developed computationally efficient algorithms for PAC and regret-minimizing RL in linear MDPs.

result Achieved sharper error or regret bounds for linear MDPs.

Hybrid RL PAC RL Regret-minimizing RL Linear MDPs Offline RL

2024-08-08abs ↗pdf ↗