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Found 14 papers, 8 papers with code
How to Correctly do Semantic Backpropagation on Language-based Agentic Systems
Language-based agentic systems have shown great promise in recent years, transitioning from solving small-scale research problems to being deployed in challenging real-world tasks.
Automatic Album Sequencing
To address this, we introduce a new user-friendly web-based tool that allows a less technical audience to upload music tracks, execute this technique in one click, and subsequently presents the result in a clean visualization to the user.
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning
The Value Iteration Network (VIN) is an end-to-end differentiable architecture that performs value iteration on a latent MDP for planning in reinforcement learning (RL).
Towards a Robust Soft Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithms
Advanced machine learning algorithms require platforms that are extremely robust and equipped with rich sensory feedback to handle extensive trial-and-error learning without relying on strong inductive biases.
Mindstorms in Natural Language-Based Societies of Mind
What should be the social structure of an NLSOM?
On Narrative Information and the Distillation of Stories
We then demonstrate how evolutionary algorithms can leverage this to extract a set of narrative templates and how these templates -- in tandem with a novel curve-fitting algorithm we introduce -- can reorder music albums to automatically induce stories in them.
Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets
Upside-Down Reinforcement Learning (UDRL) is an approach for solving RL problems that does not require value functions and uses only supervised learning, where the targets for given inputs in a dataset do not change over time.
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL
Upside down reinforcement learning (UDRL) flips the conventional use of the return in the objective function in RL upside down, by taking returns as input and predicting actions.
Learning Relative Return Policies With Upside-Down Reinforcement Learning
Lately, there has been a resurgence of interest in using supervised learning to solve reinforcement learning problems.
Automatic Embedding of Stories Into Collections of Independent Media
We look at how machine learning techniques that derive properties of items in a collection of independent media can be used to automatically embed stories into such collections.
Reward-Weighted Regression Converges to a Global Optimum
Reward-Weighted Regression (RWR) belongs to a family of widely known iterative Reinforcement Learning algorithms based on the Expectation-Maximization framework.
Does the Adam Optimizer Exacerbate Catastrophic Forgetting?
Catastrophic forgetting remains a severe hindrance to the broad application of artificial neural networks (ANNs), however, it continues to be a poorly understood phenomenon.
Universal Successor Features for Transfer Reinforcement Learning
Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks.
Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods
This paper investigates estimating the variance of a temporal-difference learning agent's update target.
