Walking the Values in Bayesian Inverse Reinforcement Learning

• URL: http://arxiv.org/abs/2407.10971v1
• Date: Mon, 15 Jul 2024 17:59:52 GMT
• Title: Walking the Values in Bayesian Inverse Reinforcement Learning
• Authors: Ondrej Bajgar, Alessandro Abate, Konstantinos Gatsis, Michael A. Osborne,
• Abstract summary: Key challenge in Bayesian IRL is bridging the computational gap between the hypothesis space of possible rewards and the likelihood. We propose ValueWalk - a new Markov chain Monte Carlo method based on this insight.
• Score: 66.68997022043075
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The goal of Bayesian inverse reinforcement learning (IRL) is recovering a posterior distribution over reward functions using a set of demonstrations from an expert optimizing for a reward unknown to the learner. The resulting posterior over rewards can then be used to synthesize an apprentice policy that performs well on the same or a similar task. A key challenge in Bayesian IRL is bridging the computational gap between the hypothesis space of possible rewards and the likelihood, often defined in terms of Q values: vanilla Bayesian IRL needs to solve the costly forward planning problem - going from rewards to the Q values - at every step of the algorithm, which may need to be done thousands of times. We propose to solve this by a simple change: instead of focusing on primarily sampling in the space of rewards, we can focus on primarily working in the space of Q-values, since the computation required to go from Q-values to reward is radically cheaper. Furthermore, this reversion of the computation makes it easy to compute the gradient allowing efficient sampling using Hamiltonian Monte Carlo. We propose ValueWalk - a new Markov chain Monte Carlo method based on this insight - and illustrate its advantages on several tasks.

Related papers

• R3HF: Reward Redistribution for Enhancing Reinforcement Learning from Human Feedback [25.27230140274847]
  Reinforcement learning from human feedback (RLHF) provides a paradigm for aligning large language models (LLMs) with human preferences. This paper proposes a novel reward redistribution method called R3HF, which facilitates a more fine-grained, token-level reward allocation.
  arXiv  Detail & Related papers  (2024-11-13T02:45:21Z)
• Bayesian Inverse Reinforcement Learning for Non-Markovian Rewards [7.2933135237680595]
  Inverse reinforcement learning (IRL) is the problem of inferring a reward function from expert behavior. A reward function might be non-Markovian, depending on more than just the current state, such as a reward machine (RM) We propose a Bayesian IRL framework for inferring RMs directly from expert behavior, requiring significant changes to the standard framework.
  arXiv  Detail & Related papers  (2024-06-20T04:41:54Z)
• Q-Probe: A Lightweight Approach to Reward Maximization for Language Models [16.801981347658625]
  We present an approach called Q-probing to adapt a pre-trained language model to maximize a task-specific reward function. At a high level, Q-probing sits between heavier approaches such as finetuning and lighter approaches such as few shot prompting.
  arXiv  Detail & Related papers  (2024-02-22T16:43:16Z)
• Dense Reward for Free in Reinforcement Learning from Human Feedback [64.92448888346125]
  We leverage the fact that the reward model contains more information than just its scalar output. We use these attention weights to redistribute the reward along the whole completion. Empirically, we show that it stabilises training, accelerates the rate of learning, and, in practical cases, may lead to better local optima.
  arXiv  Detail & Related papers  (2024-02-01T17:10:35Z)
• STARC: A General Framework For Quantifying Differences Between Reward Functions [55.33869271912095]
  We provide a class of pseudometrics on the space of all reward functions that we call STARC metrics. We show that STARC metrics induce both an upper and a lower bound on worst-case regret. We also identify a number of issues with reward metrics proposed by earlier works.
  arXiv  Detail & Related papers  (2023-09-26T20:31:19Z)
• Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees [56.848265937921354]
  Inverse reinforcement learning (IRL) aims to recover the reward function and the associated optimal policy. Many algorithms for IRL have an inherently nested structure. We develop a novel single-loop algorithm for IRL that does not compromise reward estimation accuracy.
  arXiv  Detail & Related papers  (2022-10-04T17:13:45Z)
• Learning Long-Term Reward Redistribution via Randomized Return Decomposition [18.47810850195995]
  We consider the problem formulation of episodic reinforcement learning with trajectory feedback. It refers to an extreme delay of reward signals, in which the agent can only obtain one reward signal at the end of each trajectory. We propose a novel reward redistribution algorithm, randomized return decomposition (RRD), to learn a proxy reward function for episodic reinforcement learning.
  arXiv  Detail & Related papers  (2021-11-26T13:23:36Z)
• Anti-Concentrated Confidence Bonuses for Scalable Exploration [57.91943847134011]
  Intrinsic rewards play a central role in handling the exploration-exploitation trade-off. We introduce emphanti-concentrated confidence bounds for efficiently approximating the elliptical bonus. We develop a practical variant for deep reinforcement learning that is competitive with contemporary intrinsic rewards on Atari benchmarks.
  arXiv  Detail & Related papers  (2021-10-21T15:25:15Z)
• Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification [133.20816939521941]
  In the standard Markov decision process formalism, users specify tasks by writing down a reward function. In many scenarios, the user is unable to describe the task in words or numbers, but can readily provide examples of what the world would look like if the task were solved. Motivated by this observation, we derive a control algorithm that aims to visit states that have a high probability of leading to successful outcomes, given only examples of successful outcome states.
  arXiv  Detail & Related papers  (2021-03-23T16:19:55Z)
• Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization [43.51553742077343]
  inverse reinforcement learning (IRL) is relevant to a variety of tasks including value alignment and robot learning from demonstration. This paper presents an IRL framework called Bayesian optimization-IRL (BO-IRL) which identifies multiple solutions consistent with the expert demonstrations.
  arXiv  Detail & Related papers  (2020-11-17T10:17:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.