Search Results for author:
Found 10 papers, 5 papers with code
"A Nova Eletricidade: Aplicações, Riscos e Tendências da IA Moderna -- "The New Electricity": Applications, Risks, and Trends in Current AI
With no intent to exhaust the subject, we explore the AI applications that are redefining sectors of the economy, impacting society and humanity.
A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning algorithms (MORL) extend standard reinforcement learning (RL) to scenarios where agents must optimize multiple---potentially conflicting---objectives, each represented by a distinct reward function.
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
Finally, we introduce a bound that characterizes the maximum utility loss (with respect to the optimal solution) incurred by the partial solutions computed by our method throughout learning.
MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments
We introduce MO-Gym, an extensible library containing a diverse set of multi-objective reinforcement learning environments.
Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer
If reward functions are expressed linearly, and the agent has previously learned a set of policies for different tasks, successor features (SFs) can be exploited to combine such policies and identify reasonable solutions for new problems.
Improving Urban Mobility: using artificial intelligence and new technologies to connect supply and demand
As the demand for mobility in our society seems to increase, the various issues centered on urban mobility are among those that worry most city inhabitants in this planet.
Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection
Non-stationary environments are challenging for reinforcement learning algorithms.
Quantitatively Assessing the Benefits of Model-driven Development in Agent-based Modeling and Simulation
The agent-based modeling and simulation (ABMS) paradigm has been used to analyze, reproduce, and predict phenomena related to many application areas.
Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control
In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent.
Temporal Network Analysis of Literary Texts
We study temporal networks of characters in literature focusing on "Alice's Adventures in Wonderland" (1865) by Lewis Carroll and the anonymous "La Chanson de Roland" (around 1100).
