Search Results for author:
Found 15 papers, 13 papers with code
Policy Optimization via Importance Sampling
Policy optimization is an effective reinforcement learning approach to solve continuous control tasks.
Parameter-Based Value Functions
We introduce a class of value functions called Parameter-Based Value Functions (PBVFs) whose inputs include the policy parameters.
Bayesian brains and the Rényi divergence
Under the Bayesian brain hypothesis, behavioural variations can be attributed to different priors over generative model parameters.
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.
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.
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.
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.
Mindstorms in Natural Language-Based Societies of Mind
What should be the social structure of an NLSOM?
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.
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.
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.
Learning Useful Representations of Recurrent Neural Network Weight Matrices
The program of an RNN is its weight matrix.
Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models
This study explores the role of cross-attention during inference in text-conditional diffusion models.
Towards a Robust Soft Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithms
We believe one of the reasons for this is the disconnect between traditional robotic design and the properties needed for open-ended, creativity-based AI systems.
