Search Results for author:
Found 14 papers, 6 papers with code
De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments
Bottom-up mass spectrometry-based proteomics is challenged by the task of identifying the peptide that generates a tandem mass spectrum.
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.
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.
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning
Furthermore, the core utility of our imagination is deeply coupled with communication.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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.
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning
Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control.
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.
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
Results from applying the R2 algorithm to instances of a two-dimensional and three-dimensional bin packing problems show that it outperforms generic Monte Carlo tree search, heuristic algorithms and integer programming solvers.
