Search Results for author:
Found 14 papers, 5 papers with code
Learning to explore when mistakes are not allowed
To address the need for safer learning, we propose a method that enables agents to learn goal-conditioned behaviors that explore without the risk of making harmful mistakes.
AFU: Actor-Free critic Updates in off-policy RL for continuous control
This paper presents AFU, an off-policy deep RL algorithm addressing in a new way the challenging "max-Q problem" in Q-learning for continuous action spaces, with a solution based on regression and conditional gradient scaling.
$Γ$-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data
Natural systems with emergent behaviors often organize along low-dimensional subsets of high-dimensional spaces.
Single-Reset Divide & Conquer Imitation Learning
Demonstrations are commonly used to speed up the learning process of Deep Reinforcement Learning algorithms.
A Definition of Open-Ended Learning Problems for Goal-Conditioned Agents
A lot of recent machine learning research papers have ``open-ended learning'' in their title.
Layered controller synthesis for dynamic multi-agent systems
In this paper we present a layered approach for multi-agent control problem, decomposed into three stages, each building upon the results of the previous one.
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.
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.
Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration
This sequential goal-reaching approach raises a problem of compatibility between successive goals: we need to ensure that the state resulting from reaching a goal is compatible with the achievement of the following goals.
Divide & Conquer Imitation Learning
In such context, Imitation Learning (IL) can be a powerful approach to bootstrap the learning process.
Exploratory State Representation Learning
Our experimental results show that the approach leads to efficient exploration in challenging environments with image observations, and to state representations that significantly accelerate learning in RL tasks.
Selection-Expansion: A Unifying Framework for Motion-Planning and Diversity Search Algorithms
In particular, we show empirically that, if the mapping is smooth enough, i. e. if two close policies in the parameter space lead to similar outcomes, then diversity algorithms tend to inherit exploration properties of MP algorithms.
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.
State Representation Learning from Demonstration
Indeed, a compact representation of such a state is beneficial to help robots grasp onto their environment for interacting.
