Offline Contextual Bandits in the Presence of New Actions

• URL: http://arxiv.org/abs/2605.18509v1
• Date: Mon, 18 May 2026 15:01:30 GMT
• Title: Offline Contextual Bandits in the Presence of New Actions
• Abstract summary: Off-policy contextual bandits (OPL) are used in recommendation systems and search engines.<n>OPL selects actions that maximize the expected reward from an existing action set.<n>We propose a new OPL method that leverages action features.<n>We show that PONA efficiently selects new actions while maintaining the overall policy performance.
• Score: 17.359266075849195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated decision-making algorithms drive applications such as recommendation systems and search engines. These algorithms often rely on off-policy contextual bandits or off-policy learning (OPL). Conventionally, OPL selects actions that maximize the expected reward from an existing action set. However, in many real-world scenarios, actions, such as news articles or video content, change continuously, and the action space evolves over time after data collection. We define actions introduced after deploying the logging policy as new actions and focus on OPL with new actions. Existing OPL methods identify optimal actions from the existing set effectively but cannot learn and select new actions because no relevant data are logged. To address this limitation, we propose a new OPL method that leverages action features. We first introduce the Local Combination PseudoInverse (LCPI) estimator for the policy gradient, generalizing the PseudoInverse estimator initially proposed for off-policy evaluation of slate bandits. LCPI controls the trade-off between reward-modeling condition and the condition for data collection regarding the action features, capturing the interaction effects among different dimensions of action features. Furthermore, we propose a generalized algorithm called Policy Optimization for Effective New Actions (PONA), which integrates LCPI, a component specialized for new action selection, with Doubly Robust (DR), which excels at learning within existing actions. We define PONA as a weighted sum of the LCPI and DR estimators, optimizing both the selection of existing and new actions, and allowing the proportion of new action selections to be adjusted by the weight parameter. Through extensive experiments, we demonstrate that PONA efficiently selects new actions while maintaining the overall policy performance as opposed to most existing methods that cannot select new actions.

Related papers

• OLIVIA: Online Learning via Inference-time Action Adaptation for Decision Making in LLM ReAct Agents [74.20327254615854]
  Large language model agents interleave reasoning, action selection, and observation to solve sequential decision-making tasks.<n>Existing inference-time adaptation methods for LLM agents mainly rely on prompting or retrieval.<n>We propose OLIVIA, an inference-time action adaptation framework for ReAct-style agents.
  arXiv  Detail & Related papers  (2026-05-11T19:28:20Z)
• Imitate Optimal Policy: Prevail and Induce Action Collapse in Policy Gradient [61.440209025381016]
  Policy reinforcement learning frequently utilize deep neural networks (DNNs) to learn a shared backbone of feature representations used to compute likelihoods in an action selection layer.<n>We show that under certain constraints, a structure resembling neural collapse, which we refer to as Action Collapse (AC), emerges.<n>We propose the Action Collapse Policy Gradient (ACPG) method, which accordingly affixes a synthetic ETF as our action selection layer.
  arXiv  Detail & Related papers  (2025-09-02T18:33:11Z)
• FDPP: Fine-tune Diffusion Policy with Human Preference [57.44575105114056]
  Fine-tuning Diffusion Policy with Human Preference learns a reward function through preference-based learning.<n>This reward is then used to fine-tune the pre-trained policy with reinforcement learning.<n>Experiments demonstrate that FDPP effectively customizes policy behavior without compromising performance.
  arXiv  Detail & Related papers  (2025-01-14T17:15:27Z)
• Learning Action Embeddings for Off-Policy Evaluation [6.385697591955264]
  Off-policy evaluation (OPE) methods allow us to compute the expected reward of a policy by using the logged data collected by a different policy. But when the number of actions is large, or certain actions are under-explored by the logging policy, existing estimators based on inverse-propensity scoring (IPS) can have a high or even infinite variance. Saito and Joachims propose marginalized IPS (MIPS) that uses action embeddings instead, which reduces the variance of IPS in large action spaces.
  arXiv  Detail & Related papers  (2023-05-06T06:44:30Z)
• Action Pick-up in Dynamic Action Space Reinforcement Learning [6.15205100319133]
  We propose an intelligent Action Pick-up (AP) algorithm to autonomously choose valuable actions that are most likely to boost performance from a set of new actions. In this paper, we first theoretically analyze and find that a prior optimal policy plays an important role in action pick-up by providing useful knowledge and experience. We then design two different AP methods: frequency-based global method and state clustering-based local method, based on the prior optimal policy.
  arXiv  Detail & Related papers  (2023-04-03T10:55:16Z)
• Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks [59.419152768018506]
  We show that any optimal policy necessarily satisfies the k-SP constraint. We propose a novel cost function that penalizes the policy violating SP constraint, instead of completely excluding it. Our experiments on MiniGrid, DeepMind Lab, Atari, and Fetch show that the proposed method significantly improves proximal policy optimization (PPO)
  arXiv  Detail & Related papers  (2021-07-13T21:39:21Z)
• Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization [15.945378631406024]
  Reinforcement learning (RL) has demonstrated impressive performance in decision-making tasks like embodied control, autonomous driving and financial trading. In many decision-making tasks, the agents often encounter the problem of executing actions under limited budgets. This paper formalizes the problem as a Sparse Action Markov Decision Process (SA-MDP), in which specific actions in the action space can only be executed for a limited time. We propose a policy optimization algorithm, Action Sparsity REgularization (ASRE), which adaptively handles each action with a distinct preference.
  arXiv  Detail & Related papers  (2021-05-18T16:50:42Z)
• Zeroth-Order Supervised Policy Improvement [94.0748002906652]
  Policy gradient (PG) algorithms have been widely used in reinforcement learning (RL) We propose Zeroth-Order Supervised Policy Improvement (ZOSPI) ZOSPI exploits the estimated value function $Q$ globally while preserving the local exploitation of the PG methods.
  arXiv  Detail & Related papers  (2020-06-11T16:49:23Z)
• Discrete Action On-Policy Learning with Action-Value Critic [72.20609919995086]
  Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension. We construct a critic to estimate action-value functions, apply it on correlated actions, and combine these critic estimated action values to control the variance of gradient estimation. These efforts result in a new discrete action on-policy RL algorithm that empirically outperforms related on-policy algorithms relying on variance control techniques.
  arXiv  Detail & Related papers  (2020-02-10T04:23:09Z)

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.