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Found 19 papers, 8 papers with code
Multi-Objective Quality-Diversity for Crystal Structure Prediction
This approach overlooks other potentially interesting materials that lie in neighbouring local minima and have different material properties such as conductivity or resistance to deformation.
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.
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces
While early QD algorithms view the objective and descriptor functions as black-box functions, novel tools have been introduced to use gradient information to accelerate the search and improve overall performance of those algorithms over continuous input spaces.
The Quality-Diversity Transformer: Generating Behavior-Conditioned Trajectories with Decision Transformers
In the context of neuroevolution, Quality-Diversity algorithms have proven effective in generating repertoires of diverse and efficient policies by relying on the definition of a behavior space.
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.
Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding Exploration
We show that MOME-PGX is between 4. 3 and 42 times more data-efficient than MOME and doubles the performance of MOME, NSGA-II and SPEA2 in challenging environments.
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.
Multi-Objective Quality Diversity Optimization
In this work, we consider the problem of Quality-Diversity (QD) optimization with multiple objectives.
Factored Action Spaces in Deep Reinforcement Learning
Very large action spaces constitute a critical challenge for deep Reinforcement Learning (RL) algorithms.
Sample efficient Quality Diversity for neural continuous control
The QD part contributes structural biases by decoupling the search for diversity from the search for high return, resulting in efficient management of the exploration-exploitation trade-off.
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning
This suggests that combining leading protein design methods with modern deep reinforcement learning is a viable path for discovering a Covid-19 cure and may accelerate design of peptide-based therapeutics for other diseases.
Offline Reinforcement Learning Hands-On
Offline Reinforcement Learning (RL) aims to turn large datasets into powerful decision-making engines without any online interactions with the environment.
Learning Compositional Neural Programs for Continuous Control
Third, the self-models are harnessed to learn recursive compositional programs with multiple levels of abstraction.
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.
Learning Compositional Neural Programs with Recursive Tree Search and Planning
AlphaZero contributes powerful neural network guided search algorithms, which we augment with recursion.
First-order and second-order variants of the gradient descent in a unified framework
In this paper, we provide an overview of first-order and second-order variants of the gradient descent method that are commonly used in machine learning.
