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Found 14 papers, 9 papers with code
A Dynamical View of the Question of Why
We address causal reasoning in multivariate time series data generated by stochastic processes.
Successor Features for Efficient Multisubject Controlled Text Generation
While large language models (LLMs) have achieved impressive performance in generating fluent and realistic text, controlling the generated text so that it exhibits properties such as safety, factuality, and non-toxicity remains challenging.
Systematic Rectification of Language Models via Dead-end Analysis
Other methods rely on rule-based or prompt-based token elimination, which are limited as they dismiss future tokens and the overall meaning of the complete discourse.
Semi-Markov Offline Reinforcement Learning for Healthcare
Finally, we apply our new algorithms to a real-world offline dataset pertaining to warfarin dosing for stroke prevention and demonstrate similar results.
Orchestrated Value Mapping for Reinforcement Learning
We present a general convergent class of reinforcement learning algorithms that is founded on two distinct principles: (1) mapping value estimates to a different space using arbitrary functions from a broad class, and (2) linearly decomposing the reward signal into multiple channels.
Medical Dead-ends and Learning to Identify High-risk States and Treatments
Machine learning has successfully framed many sequential decision making problems as either supervised prediction, or optimal decision-making policy identification via reinforcement learning.
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
We propose the k-Shortest-Path (k-SP) constraint: a novel constraint on the agent's trajectory that improves the sample efficiency in sparse-reward MDPs.
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing hypothetical treatment strategies for septic patients, with a particular focus on offline learning with observational data.
Learning to Represent Action Values as a Hypergraph on the Action Vertices
To test this, we set forth the action hypergraph networks framework -- a class of functions for learning action representations in multi-dimensional discrete action spaces with a structural inductive bias.
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
In an effort to better understand the different ways in which the discount factor affects the optimization process in reinforcement learning, we designed a set of experiments to study each effect in isolation.
Hybrid Reward Architecture for Reinforcement Learning
One of the main challenges in reinforcement learning (RL) is generalisation.
Multi-Advisor Reinforcement Learning
We consider tackling a single-agent RL problem by distributing it to $n$ learners.
Separation of Concerns in Reinforcement Learning
In this paper, we propose a framework for solving a single-agent task by using multiple agents, each focusing on different aspects of the task.
Policy Networks with Two-Stage Training for Dialogue Systems
Indeed, with only a few hundred dialogues collected with a handcrafted policy, the actor-critic deep learner is considerably bootstrapped from a combination of supervised and batch RL.
