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Found 10 papers, 4 papers with code
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration
QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax.
PASTA: Pretrained Action-State Transformer Agents
Self-supervised learning has brought about a revolutionary paradigm shift in various computing domains, including NLP, vision, and biology.
Evolving Populations of Diverse RL Agents with MAP-Elites
Quality Diversity (QD) has emerged as a powerful alternative optimization paradigm that aims at generating large and diverse collections of solutions, notably with its flagship algorithm MAP-ELITES (ME) which evolves solutions through mutations and crossovers.
Assessing Quality-Diversity Neuro-Evolution Algorithms Performance in Hard Exploration Problems
Exploration is at the heart of several domains trying to solve control problems such as Reinforcement Learning and QD methods are promising candidates to overcome the challenges associated.
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery
Recent work has shown that training a mixture of policies, as opposed to a single one, that are driven to explore different regions of the state-action space can address this shortcoming by generating a diverse set of behaviors, referred to as skills, that can be collectively used to great effect in adaptation tasks or for hierarchical planning.
Fast Population-Based Reinforcement Learning on a Single Machine
Training populations of agents has demonstrated great promise in Reinforcement Learning for stabilizing training, improving exploration and asymptotic performance, and generating a diverse set of solutions.
Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization
This paper proposes a novel algorithm, QDPG, which combines the strength of Policy Gradient algorithms and Quality Diversity approaches to produce a collection of diverse and high-performing neural policies in continuous control environments.
Online Learning with a Hint
We show that the player can benefit from such a hint if the set of feasible actions is sufficiently round.
Real-Time Bidding with Side Information
We consider the problem of repeated bidding in online advertising auctions when some side information (e. g. browser cookies) is available ahead of submitting a bid in the form of a $d$-dimensional vector.
No-Regret Learnability for Piecewise Linear Losses
In the convex optimization approach to online regret minimization, many methods have been developed to guarantee a $O(\sqrt{T})$ bound on regret for subdifferentiable convex loss functions with bounded subgradients, by using a reduction to linear loss functions.
