Authors :
Jesu Narkarunai Arasu Malaiyappan; Sai Mani Krishna Sistla; Jawaharbabu Jeyaraman
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/n8x2kkzm
Scribd :
https://tinyurl.com/ker42ms3
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR983
Abstract :
Reinforcement learning, often known as RL,
has developed as a strong paradigm to teach autonomous
software agents to make choices in contexts that are both
complicated and dynamic. This abstract investigates
recent developments and uses of RL in a variety of fields,
showing both its transformational potential and the
constraints that it faces at present. Recent developments
in reinforcement learning (RL) algorithms, in particular
deep reinforcement learning (DRL), have made it possible
to make major advancements in autonomous decision-
making tasks. DRL algorithms can learn complicated
representations of state-action spaces by using deep
neural networks. This allows for more efficient
exploration and exploitation methods to be implemented.
Additionally, advancements in algorithmic
enhancements, such as prioritized experience replay and
distributional reinforcement learning, have improved the
stability and sample efficiency of reinforcement learning
algorithms, which has made it possible for these
algorithms to be used in real-world applications.
Robotics, autonomous cars, game playing, finance,
and healthcare are just a few of the many fields that may
benefit from the use of RL. In the field of robotics,
reinforcement learning (RL) makes it possible for
autonomous agents to learn how to navigate, manipulate,
and move about in environments that are both
complicated and unstructured. To improve both safety
and efficiency on the road, autonomous cars make use of
reinforcement learning (RL) to make decisions in
dynamic traffic situations. In finance, RL algorithms are
used for portfolio optimization, algorithmic trading, and
risk management. These applications serve to improve
investment techniques and decision-making procedures.
Furthermore, in the field of healthcare, RL supports
individualized treatment planning, clinical decision
support, and medical image analysis, which enables
physicians to provide patients with care that is specifically
suited to their needs. Despite the promising improvements
and applications, RL is still confronted with several
difficulties that restrict its capacity to be widely adopted
and scaled. Among these problems are the inefficiency of
the sample, the trade-offs between exploration and
exploitation, concerns about safety and dependability,
and the need for explainability and interpretability in
decision-making processes. To effectively address these
difficulties, it is necessary to engage in collaborative
efforts across several disciplines, conduct research on
algorithmic developments, and establish extensive
assessment frameworks (Anon, 2022).
Reinforcement learning, often known as RL,
has developed as a strong paradigm to teach autonomous
software agents to make choices in contexts that are both
complicated and dynamic. This abstract investigates
recent developments and uses of RL in a variety of fields,
showing both its transformational potential and the
constraints that it faces at present. Recent developments
in reinforcement learning (RL) algorithms, in particular
deep reinforcement learning (DRL), have made it possible
to make major advancements in autonomous decision-
making tasks. DRL algorithms can learn complicated
representations of state-action spaces by using deep
neural networks. This allows for more efficient
exploration and exploitation methods to be implemented.
Additionally, advancements in algorithmic
enhancements, such as prioritized experience replay and
distributional reinforcement learning, have improved the
stability and sample efficiency of reinforcement learning
algorithms, which has made it possible for these
algorithms to be used in real-world applications.
Robotics, autonomous cars, game playing, finance,
and healthcare are just a few of the many fields that may
benefit from the use of RL. In the field of robotics,
reinforcement learning (RL) makes it possible for
autonomous agents to learn how to navigate, manipulate,
and move about in environments that are both
complicated and unstructured. To improve both safety
and efficiency on the road, autonomous cars make use of
reinforcement learning (RL) to make decisions in
dynamic traffic situations. In finance, RL algorithms are
used for portfolio optimization, algorithmic trading, and
risk management. These applications serve to improve
investment techniques and decision-making procedures.
Furthermore, in the field of healthcare, RL supports
individualized treatment planning, clinical decision
support, and medical image analysis, which enables
physicians to provide patients with care that is specifically
suited to their needs. Despite the promising improvements
and applications, RL is still confronted with several
difficulties that restrict its capacity to be widely adopted
and scaled. Among these problems are the inefficiency of
the sample, the trade-offs between exploration and
exploitation, concerns about safety and dependability,
and the need for explainability and interpretability in
decision-making processes. To effectively address these
difficulties, it is necessary to engage in collaborative
efforts across several disciplines, conduct research on
algorithmic developments, and establish extensive
assessment frameworks (Anon, 2022).
