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TD-regularized actor-critic methods
Actor-critic methods can achieve incredible performance on difficult reinforcement learning
problems, but they are also prone to instability. This is partly due to the interaction between …
problems, but they are also prone to instability. This is partly due to the interaction between …
Path consistency learning in tsallis entropy regularized mdps
We study the sparse entropy-regularized reinforcement learning (ERL) problem in which the
entropy term is a special form of the Tsallis entropy. The optimal policy of this formulation is …
entropy term is a special form of the Tsallis entropy. The optimal policy of this formulation is …
[PDF][PDF] Risk-Sensitive Reinforcement Learning with φ-Divergence-Risk
Standard reinforcement learning (RL) algorithms primarily focus on minimizing the expected
sum of costs, which can be insufficient in contexts where risk sensitivity is crucial. This paper …
sum of costs, which can be insufficient in contexts where risk sensitivity is crucial. This paper …
Divergence-augmented policy optimization
In deep reinforcement learning, policy optimization methods need to deal with issues such
as function approximation and the reuse of off-policy data. Standard policy gradient methods …
as function approximation and the reuse of off-policy data. Standard policy gradient methods …
Co-evolution of predator-prey ecosystems by reinforcement learning agents
The problem of finding adequate population models in ecology is important for
understanding essential aspects of their dynamic nature. Since analyzing and accurately …
understanding essential aspects of their dynamic nature. Since analyzing and accurately …
f-Betas and portfolio optimization with f-divergence induced risk measures
R Ding - Quantitative Finance, 2023 - Taylor & Francis
In this paper, we build on using the class of f-divergence induced coherent risk measures for
portfolio optimization and derive its necessary optimality conditions formulated in CAPM …
portfolio optimization and derive its necessary optimality conditions formulated in CAPM …
Model-based reinforcement learning via stochastic hybrid models
Optimal control of general nonlinear systems is a central challenge in automation. Enabled
by powerful function approximators, data-driven approaches to control have recently …
by powerful function approximators, data-driven approaches to control have recently …
[PDF][PDF] Path consistency learning in tsallis entropy regularized mdps
We study the sparse entropy-regularized reinforcement learning (ERL) problem in which the
entropy term is a special form of the Tsallis entropy. The optimal policy of this formulation is …
entropy term is a special form of the Tsallis entropy. The optimal policy of this formulation is …
[PDF][PDF] Risk-Sensitive Reinforcement Learning with Coherent Risk Measures
X NI - 2025 - faculty.engineering.ucdavis.edu
Reinforcement Learning (RL) is a branch of machine learning that focuses on training
agents to make sequential decisions. By interacting with the environment, an RL agent …
agents to make sequential decisions. By interacting with the environment, an RL agent …
[HTML][HTML] Statistical Machine Learning for Modeling and Control of Stochastic Structured Systems
H Abdulsamad - 2022 - tubiblio.ulb.tu-darmstadt.de
Machine learning and its various applications have driven innovation in robotics, synthetic
perception, and data analytics. The last decade especially has experienced an explosion in …
perception, and data analytics. The last decade especially has experienced an explosion in …