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Found 21 papers, 16 papers with code
On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers
This article provides a rigorous analysis of convergence and stability of Episodic Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning and Online Decision Transformers.
Upside Down Reinforcement Learning with Policy Generators
Our method, dubbed Upside Down Reinforcement Learning with Policy Generators (UDRLPG), streamlines comparable techniques by removing the need for an evaluator or critic to update the weights of the generator.
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.
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).
Highway Value Iteration Networks
To address this problem, we embed highway value iteration -- a recent algorithm designed to facilitate long-term credit assignment -- into the structure of VINs.
Highway Reinforcement Learning
We show, however, that such IS-free methods underestimate the optimal value function (VF), especially for large $n$, restricting their capacity to efficiently utilize information from distant future time steps.
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.
Faster Diffusion via Temporal Attention Decomposition
We explore the role of attention mechanism during inference in text-conditional diffusion models.
Learning Useful Representations of Recurrent Neural Network Weight Matrices
Recurrent Neural Networks (RNNs) are general-purpose parallel-sequential computers.
Language Agents as Optimizable Graphs
Various human-designed prompt engineering techniques have been proposed to improve problem solvers based on Large Language Models (LLMs), yielding many disparate code bases.
The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute
We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours.
Learning to Identify Critical States for Reinforcement Learning from Videos
Recent work on deep reinforcement learning (DRL) has pointed out that algorithmic information about good policies can be extracted from offline data which lack explicit information about executed actions.
Mindstorms in Natural Language-Based Societies of Mind
What should be the social structure of an NLSOM?
Goal-Conditioned Generators of Deep Policies
A form of weight-sharing HyperNetworks and policy embeddings scales our method to generate deep NNs.
General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States
In continuous control problems with infinitely many states, our value function minimizes its prediction error by simultaneously learning a small set of `probing states' and a mapping from actions produced in probing states to the policy's return.
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules
Neural ordinary differential equations (ODEs) have attracted much attention as continuous-time counterparts of deep residual neural networks (NNs), and numerous extensions for recurrent NNs have been proposed.
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.
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.
Bayesian brains and the Rényi divergence
Under the Bayesian brain hypothesis, behavioural variations can be attributed to different priors over generative model parameters.
Parameter-Based Value Functions
We introduce a class of value functions called Parameter-Based Value Functions (PBVFs) whose inputs include the policy parameters.
Policy Optimization via Importance Sampling
Policy optimization is an effective reinforcement learning approach to solve continuous control tasks.
