Transformers have significantly impacted domains like natural language processing,
computer vision, and robotics, where they improve performance compared to other neural …

The energy consumed by commercial buildings for heating and cooling is significantly
increased. To better cope with the uncertainty introduced by the high penetration of …

Many important real-world Reinforcement Learning (RL) problems involve partial
observability and require policies with memory. Unfortunately, standard deep RL algorithms …

Reinforcement Learning (RL) is a machine learning paradigm wherein an artificial agent
interacts with an environment with the purpose of learning behaviour that maximizes the …

Reinforcement Learning faces an important challenge in partially observable environments
with long-term dependencies. In order to learn in an ambiguous environment, an agent has …

Abstract (EN) This thesis is divided into two parts dealing with the optimization problems in
Markov decision processes (MDPs) and different neural network-based numerical solvers …

Value-based reinforcement learning is the current State-Of-The-Art due to high sampling
efficiency. However, our study shows it suffers from low exploitation in early training period …

Value-based reinforcement learning is the current State-Of-The-Art due to high sampling
efficiency. However, our study shows it suffers from low exploitation in early training period …

Reinforcement learning is generally difficult for partially observable Markov decision
processes (POMDPs), which occurs when the agent's observation is partial or noisy. To seek …