Recent works successfully leveraged Large Language Models' (LLM) abiliti...
Deep Reinforcement Learning has been successfully applied to learn robot...
Teaching an agent to perform new tasks using natural language can easily...
The SWIMMER environment is a standard benchmark in reinforcement learnin...
Reinforcement learning (RL) in long horizon and sparse reward tasks is
n...
Multi-goal Reinforcement Learning has recently attracted a large amount ...
Learning from demonstration methods usually leverage close to optimal
de...
When cast into the Deep Reinforcement Learning framework, many robotics ...
Although humans live in an open-ended world and endlessly face new
chall...
Deep neuroevolution and deep Reinforcement Learning have received a lot ...
When demonstrating a task, human tutors pedagogically modify their behav...
In the quest for autonomous agents learning open-ended repertoires of sk...
Autonomous discovery and direct instruction are two extreme sources of
l...
Reinforcement learning agents need a reward signal to learn successful
p...
Building autonomous machines that can explore open-ended environments,
d...
Offline Reinforcement Learning (RL) aims to turn large datasets into pow...
We propose a novel solution to challenging sparse-reward, continuous con...
We propose a novel reinforcement learning algorithm,QD-RL, that incorpor...
Intrinsically motivated agents freely explore their environment and set ...
In the real world, linguistic agents are also embodied agents: they perc...
Robots are still limited to controlled conditions, that the robot design...
The exploration-exploitation trade-off is at the heart of reinforcement
...
Weight-sharing (WS) has recently emerged as a paradigm to accelerate the...
In environments with continuous state and action spaces, state-of-the-ar...
We propose a novel reinforcement learning algorithm, AlphaNPI, that
inco...
Consistently checking the statistical significance of experimental resul...
In this paper, we propose a framework that enables a human teacher to sh...
In this paper, we propose an unsupervised reinforcement learning agent c...
In this paper, we provide an overview of first-order and second-order
va...
In open-ended and changing environments, agents face a wide range of
pot...
Perceiving the surrounding environment in terms of objects is useful for...
Deep neuroevolution and deep reinforcement learning (deep RL) algorithms...
Deep neuroevolution, that is evolutionary policy search methods based on...
In this paper, we investigate a new form of automated curriculum learnin...
Consistently checking the statistical significance of experimental resul...
Continuous action policy search, the search for efficient policies in
co...
Intrinsically motivated goal exploration algorithms enable machines to
d...
In continuous action domains, standard deep reinforcement learning algor...
SDYNA is a general framework designed to address large stochastic
reinfo...