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.
Proof of convergence for multi-objective optimization using inverse reinforcement learning.
Proof shows imitation of expert's reward and solutions in multi-objective optimization.
Paper proposes IRL methods for limited interaction scenarios.
This work simplifies IRL by using potential-based reward shaping.
We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous work on Bayesian inverse reinforcement learning and allows us to obtain a posterior distribution on the agent's preferences, policy and optio…
Develops statistical framework for resolving reward function ambiguity in inverse reinforcement learning.
Combines human and AI to optimize fund managers' investment decisions.
AIRL learns robust, generalizable reward functions from demonstrations.
Modeling driver trajectories using inverse reinforcement learning and random utility.
Improves imitation learning in RL by learning reward function efficiently.
This paper sets a lower bound for sample complexity in inverse reinforcement learning.
Inverse reinforcement learning (IRL) is the problem of finding a reward function that generates a given optimal policy for a given Markov Decision Process. This paper looks at an algorithmic-independent geometric analysis of the IRL problem with finite states and actions. A L1-regularized Support Vector Machine formula…
The design of a reward function often poses a major practical challenge to real-world applications of reinforcement learning. Approaches such as inverse reinforcement learning attempt to overcome this challenge, but require expert demonstrations, which can be difficult or expensive to obtain in practice. We propose var…
Reinforcement learning in complex environments is a challenging problem. In particular, the success of reinforcement learning algorithms depends on a well-designed reward function. Inverse reinforcement learning (IRL) solves the problem of recovering reward functions from expert demonstrations. In this paper, we solve …
New approach uses inverse reinforcement learning to improve language model training.
We address the problem of inverse reinforcement learning in Markov decision processes where the agent is risk-sensitive. In particular, we model risk-sensitivity in a reinforcement learning framework by making use of models of human decision-making having their origins in behavioral psychology, behavioral economics, an…
Rewriting history improves RL algorithms for solving multiple tasks.
Augmenting reinforcement learning with imitation learning is often hailed as a method by which to improve upon learning from scratch. However, most existing methods for integrating these two techniques are subject to several strong assumptions---chief among them that information about demonstrator actions is available.…
New algorithm reduces performance loss in IRL with mismatched transition dynamics.
New approach transfers rewards learned in one environment to reinforcement learning in a new environment.
Reinforcement learning agents are prone to undesired behaviors due to reward mis-specification. Finding a set of reward functions to properly guide agent behaviors is particularly challenging in multi-agent scenarios. Inverse reinforcement learning provides a framework to automatically acquire suitable reward functions…
Study shows fast rates for inverse reinforcement learning with linear rewards.
This paper considers the problem of inverse reinforcement learning in zero-sum stochastic games when expert demonstrations are known to be not optimal. Compared to previous works that decouple agents in the game by assuming optimality in expert strategies, we introduce a new objective function that directly pits expert…
Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning. Existing work has explored a variety of active query strategies; however, to our knowledge, none of these strategies directly minimize the performance risk of the policy the robot is learning. U…
The goal of the inverse reinforcement learning (IRL) problem is to recover the reward functions from expert demonstrations. However, the IRL problem like any ill-posed inverse problem suffers the congenital defect that the policy may be optimal for many reward functions, and expert demonstrations may be optimal for man…
Providing a suitable reward function to reinforcement learning can be difficult in many real world applications. While inverse reinforcement learning (IRL) holds promise for automatically learning reward functions from demonstrations, several major challenges remain. First, existing IRL methods learn reward functions f…
We consider the problem of learning by demonstration from agents acting in unknown stochastic Markov environments or games. Our aim is to estimate agent preferences in order to construct improved policies for the same task that the agents are trying to solve. To do so, we extend previous probabilistic approaches for in…
We consider the problem of learning by demonstration from agents acting in unknown stochastic Markov environments or games. Our aim is to estimate agent preferences in order to construct improved policies for the same task that the agents are trying to solve. To do so, we extend previous probabilistic approaches for in…
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations. Our model stacks multiple latent GP layers to learn abstract representations of the state feature space, which is link…
New IRL algorithm for continuous state spaces with formal guarantees.
New algorithms speed up inverse reinforcement learning by solving MDPs once.
Novel IRL method identifies suboptimal medical decisions in ICU data.
Paper presents a rank-1 approximation method for natural policy gradients in deep RL.
Paper analyzes AIRL in high-dimensional spaces using random matrix theory.
Reward shaping speeds up human learning through IRL.
AceIRL learns expert reward from active exploration.
Paper tackles efficient IRL in offline settings with polynomial samples and runtime.
This paper addresses the problem of learning a task from demonstration. We adopt the framework of inverse reinforcement learning, where tasks are represented in the form of a reward function. Our contribution is a novel active learning algorithm that enables the learning agent to query the expert for more informative d…
We consider a problem of learning the reward and policy from expert examples under unknown dynamics. Our proposed method builds on the framework of generative adversarial networks and introduces the empowerment-regularized maximum-entropy inverse reinforcement learning to learn near-optimal rewards and policies. Empowe…
Active IRL selects optimal human demonstrations for learning AI preferences.
Paper proposes a method to learn and exceed expert demonstrations in unknown reward environments.
Robo-advisor uses ML to optimize investment performance.
In the field of reinforcement learning there has been recent progress towards safety and high-confidence bounds on policy performance. However, to our knowledge, no practical methods exist for determining high-confidence policy performance bounds in the inverse reinforcement learning setting---where the true reward fun…
One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution. This assumption deviates from actual human behaviors under ambiguity. Risk-sensitive inverse reinforcement learning (RS-IRL) bridges such gap by assum…
New algorithm infers reward function from agent's learning trajectories.
Bayesian method estimates dynamics from near-optimal trajectories.
The paper solves IRL for Bayesian stopping time problems.
PQR estimates reward functions from actions and states without assuming state-only rewards.