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Found 15 papers, 2 papers with code
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates.
Preference Elicitation with Soft Attributes in Interactive Recommendation
Leveraging concept activation vectors for soft attribute semantics, we develop novel preference elicitation methods that can accommodate soft attributes and bring together both item and attribute-based preference elicitation.
Factual and Personalized Recommendations using Language Models and Reinforcement Learning
Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences.
Demystifying Embedding Spaces using Large Language Models
Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format.
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning
Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans "in the wild" remains a formidable challenge.
A Mixture-of-Expert Approach to RL-based Dialogue Management
Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge.
Efficient Risk-Averse Reinforcement Learning
In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the returns.
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition
However, we identify these techniques are not well equipped for safe policy learning because they ignore negative experiences(e. g., unsafe or unsuccessful), focusing only on positive experiences, which harms their ability to generalize to new tasks safely.
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors
Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e. g., clicks, item consumption, ratings).
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition
Though many reinforcement learning (RL) problems involve learning policies in settings that are difficult to specify safety constraints and sparse rewards, current methods struggle to rapidly and safely acquire successful policies.
Non-Stationary Latent Bandits
The key idea is to frame this problem as a latent bandit, where the prototypical models of user behavior are learned offline and the latent state of the user is inferred online from its interactions with the models.
CoinDICE: Off-Policy Confidence Interval Estimation
We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning, where the goal is to estimate a confidence interval on a target policy's value, given only access to a static experience dataset collected by unknown behavior policies.
Safe Reinforcement Learning with Natural Language Constraints
We then develop an agent with a modular architecture that can interpret and adhere to such textual constraints while learning new tasks.
Safe Policy Learning for Continuous Control
We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i. e.,~policies that keep the agent in desirable situations, both during training and at convergence.
Lyapunov-based Safe Policy Optimization
In many reinforcement learning applications, it is crucial that the agent interacts with the environment only through safe policies, i. e.,~policies that do not take the agent to certain undesirable situations.
