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Found 19 papers, 9 papers with code
"Give Me an Example Like This": Episodic Active Reinforcement Learning from Demonstrations
This paper presents EARLY (Episodic Active Learning from demonstration querY), an algorithm that enables a learning agent to generate optimized queries of expert demonstrations in a trajectory-based feature space.
Emergence of specialized Collective Behaviors in Evolving Heterogeneous Swarms
This is supported by phenotypic plasticity: individuals sharing the same genotype that is expressed differently for different classes of individuals, each specializing in one task.
Environment induced emergence of collective behaviour in evolving swarms with limited sensing
Designing controllers for robot swarms is challenging, because human developers have typically no good understanding of the link between the details of a controller that governs individual robots and the swarm behavior that is an indirect result of the interactions between swarm members and the environment.
Comparing lifetime learning methods for morphologically evolving robots
Evolving morphologies and controllers of robots simultaneously leads to a problem: Even if the parents have well-matching bodies and brains, the stochastic recombination can break this match and cause a body-brain mismatch in their offspring.
Heritability in Morphological Robot Evolution
In the field of evolutionary robotics, choosing the correct encoding is very complicated, especially when robots evolve both behaviours and morphologies at the same time.
Impact of Energy Efficiency on the Morphology and Behaviour of Evolved Robots
In this paper, we mitigate this problem by extending our simulator with a battery model and taking energy consumption into account during fitness evaluations.
Behavior-based Neuroevolutionary Training in Reinforcement Learning
In addition to their undisputed success in solving classical optimization problems, neuroevolutionary and population-based algorithms have become an alternative to standard reinforcement learning methods.
A coevolutionary approach to deep multi-agent reinforcement learning
Our results show that these Deep Coevolutionary algorithms (1) can be successfully trained to play various games, (2) outperform Ape-X DQN in some of them, and therefore (3) show that Coevolution can be a viable approach to solving complex multi-agent decision-making problems.
pH-RL: A personalization architecture to bring reinforcement learning to health practice
We implemented our open-source RL architecture and integrated it with the MoodBuster mobile application for mental health to provide messages to increase daily adherence to the online therapeutic modules.
Generating Human-Like Movement: A Comparison Between Two Approaches Based on Environmental Features
Modelling realistic human behaviours in simulation is an ongoing challenge that resides between several fields like social sciences, philosophy, and artificial intelligence.
Learning Locomotion Skills in Evolvable Robots
The challenge of robotic reproduction -- making of new robots by recombining two existing ones -- has been recently cracked and physically evolving robot systems have come within reach.
EVO-RL: Evolutionary-Driven Reinforcement Learning
Furthermore, we propose that this distinction is decided by the evolutionary process, thus allowing evo-RL to be adaptive to different environments.
Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm
Evolutionary Algorithms (EA) on the other hand rely on search heuristics that typically do not depend on all previous data and can be done in constant time.
Learning Directed Locomotion in Modular Robots with Evolvable Morphologies
We generalize the well-studied problem of gait learning in modular robots in two dimensions.
EvoMan: Game-playing Competition
For this particular competition, the main goal is to beat all of the eight bosses using a generalist strategy.
Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning
In detail, we investigate a) the potential of SMB-NE with respect to evaluation efficiency and b) how to select adequate input sets for the phenotypic distance measure in a reinforcement learning problem.
A new Taxonomy of Continuous Global Optimization Algorithms
The extracted features of components or operators allow us to create a set of classification indicators to distinguish between a small number of classes.
A clustering-based reinforcement learning approach for tailored personalization of e-Health interventions
The time to learn intervention policies is limited as disengagement from the user can occur quickly.
Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO
The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem.
