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Found 12 papers, 2 papers with code
RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs
A promising approach is reinforcement learning from human feedback (RLHF), which leverages human feedback to update the model in accordance with human preferences and mitigate issues like toxicity and hallucinations.
From Past to Future: Rethinking Eligibility Traces
In this paper, we introduce a fresh perspective on the challenges of credit assignment and policy evaluation.
Coagent Networks: Generalized and Scaled
However, the coagent framework is not just an alternative to BDL; the two approaches can be blended: BDL can be combined with coagent learning rules to create architectures with the advantages of both approaches.
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments
Methods for sequential decision-making are often built upon a foundational assumption that the underlying decision process is stationary.
Model-Based Reinforcement Learning with SINDy
We found that an optimal policy trained on the discovered dynamics of the underlying system can generalize well.
Model-based Reinforcement Learning
reinforcement-learning
+1
Enforcing Delayed-Impact Fairness Guarantees
Recent research has shown that seemingly fair machine learning models, when used to inform decisions that have an impact on peoples' lives or well-being (e. g., applications involving education, employment, and lending), can inadvertently increase social inequality in the long term.
Fairness Guarantees under Demographic Shift
Recent studies have demonstrated that using machine learning for social applications can lead to injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes.
Universal Off-Policy Evaluation
When faced with sequential decision-making problems, it is often useful to be able to predict what would happen if decisions were made using a new policy.
Autonomous learning of multiple, context-dependent tasks
These situations pose two challenges: (a) to recognise the different contexts that need different policies; (b) quickly learn the policies to accomplish the same tasks in the new discovered contexts.
Optimal Options for Multi-Task Reinforcement Learning Under Time Constraints
Reinforcement learning can greatly benefit from the use of options as a way of encoding recurring behaviours and to foster exploration.
Autonomous Open-Ended Learning of Interdependent Tasks
Autonomy is fundamental for artificial agents acting in complex real-world scenarios.
On Ensuring that Intelligent Machines Are Well-Behaved
We propose a new framework for designing machine learning algorithms that simplifies the problem of specifying and regulating undesirable behaviors.
