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Found 12 papers, 4 papers with code
Gemma: Open Models Based on Gemini Research and Technology
This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models.
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning
However, due to safety, ethical, and practicality constraints, this type of trial-and-error experimentation is often infeasible in many real-world domains such as healthcare and robotics.
Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach
Actor-critic algorithms that make use of distributional policy evaluation have frequently been shown to outperform their non-distributional counterparts on many challenging control tasks.
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning
Many advances that have improved the robustness and efficiency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional objectives or constraints in the policy optimization step.
Critic Regularized Regression
Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction.
Acme: A Research Framework for Distributed Reinforcement Learning
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions.
Which Learning Algorithms Can Generalize Identity-Based Rules to Novel Inputs?
We propose a novel framework for the analysis of learning algorithms that allows us to say when such algorithms can and cannot generalize certain patterns from training data to test data.
Unbounded Bayesian Optimization via Regularization
Bayesian optimization has recently emerged as a popular and efficient tool for global optimization and hyperparameter tuning.
Heteroscedastic Treed Bayesian Optimisation
At the core of this approach is a Gaussian process prior that captures our belief about the distribution over functions.
An Entropy Search Portfolio for Bayesian Optimization
How- ever, the performance of a Bayesian optimization method very much depends on its exploration strategy, i. e. the choice of acquisition function, and it is not clear a priori which choice will result in superior performance.
Exploiting correlation and budget constraints in Bayesian multi-armed bandit optimization
This problem is also known as fixed-budget best arm identification in the multi-armed bandit literature.
