Reinforcement learning (RL) is a key area of artificial intelligence focused on training agents to make decisions through interaction with environments, achieving significant breakthroughs in recent years, including surpassing human performance in various games. The document discusses the elements, types, and applications of RL, alongside the challenges faced, such as the exploration-exploitation dilemma and high computational demands. Looking forward, the future of RL is promising, with potential advancements through interdisciplinary integration that could overcome existing limitations.

1. 7376212CB107
ARCHAYA R S
REINFORCEMENT
LEARNING
and its impact on the future.

2. It is important to understand
the utilities of artificial
intelligence
New future
01 - What is it?
02 - Introduction
03 - Elements of RL
04 - Types of RL
05 - Application of RL
06 - Compare RL & SL
07 - Challenges of RL
08 - Conclusion

3. Reinforcement learning is an area of Artificial
Intelligence; it has emerged as an effective tool
towards building artificially intelligent systems
and solving sequential decision-making
problems. Reinforcement Learning has
achieved many impressive breakthroughs in
recent years and it was able to surpass the
human level in many fields; it is able to play and
win various games. Historically, reinforcement
learning was efficient in solving some control
system problems. Nowadays, it has a
growing range of applications.
ABSTRACT
01 - What is it?

4. Artificial
Human Intelligence
INTRODUCTION
The upraise of Artificial Intelligence is associated with Deep
Learning achievements in recent years. Deep Learning is
basically a set of multiple layers of neural networks connected to
each other. Reinforcement learning is learning through
interaction with an environment by taking different actions and
experiencing many failures and successes while trying to
maximize the received rewards. It is close to human learning. The
algorithm learns a policy of how to act in the environment. It is a
problem faced by an agent that learns behavior through trail-
and-error interactions with a dynamic environment.

5. • AGENT: Intelligent Program
• ENVIRONMENT: External Condition
• POLICY: Mapping from states to actions
• REWARD: Defines the goal in the RL problem,
Policy is altered to achieve this goal.
• VALUE: The value of a state is the total
amount of reward an agent can expect to
accumulate over the future, starting from that
state.
• MODEL OF ENVIRONMENT: Predict the
mimic behavior of the environment, then
predict the resultant of the next state and
reward.
ELEMENTS OF RL

6. Positive: Positive Reinforcement is defined as when an event,
occurs due to a particular behavior, and increases the strength
and the frequency of the behavior.
• Maximizes Performance
• Sustain Change for a long period of time
Negative: Negative Reinforcement is defined as the
strengthening of behavior because a negative condition is
stopped or avoided.
• Increases Behavior
• Provide defiance to a minimum standard of performance
• It Only provides enough to meet up the minimum behavior
TYPES OF REINFORCEMENT LEARNING

7. refinery’s
operation in
real time.
RL can be used
in large
environments
in the following
situations:
• A model of
the
environmen
t is known,
but an
analytic
solution is
not
available;
• Only a
APPLICATIONS OF RL:

8. Reinforcement learning, while high in potential, can
be difficult to deploy and remains limited in its
application. One of the barriers to the deployment of
this type of machine learning is its reliance on an
exploration of the environment.
For example, if you were to deploy a robot that was
reliant on reinforcement learning to navigate a complex
physical environment, it will seek new states and take
different actions as it moves. It is difficult to consistently
take the best actions in a real-world environment,
however, because of how frequently the environment
changes.
CHALLENGES OF APPLYING REINFORCEMENT LEARNING

9. COMPARING
REINFORCEMENT AND
SUPERVISED
LEARNING

10. However, it's crucial to acknowledge the existing
challenges and limitations. The exploration-exploitation
dilemma, high computational demands, and the need for
careful reward engineering remain obstacles that
researchers and practitioners must address. Striking a
balance between efficient learning and generalization
while minimizing negative societal impacts requires
ethical considerations to be woven into the fabric of
reinforcement learning research.
Looking ahead, the future of reinforcement learning
holds exciting prospects. Advances in algorithms, such as
deep reinforcement learning, along with the integration
of techniques from other disciplines like neuroscience,
psychology, and multi-agent systems, could lead to
breakthroughs in addressing current limitations.
CONCLUSI
ON

11. THANK
YOU