Reinforcement learning is being used in an esports project to train non-player characters (NPCs). The NPCs receive rewards for correct actions and penalties for incorrect actions. This reinforcement learning technique could also be used to train an AI car in a game to navigate to a destination without crashing into trees by providing positive feedback for successful trips and negative feedback for crashes. Reinforcement learning is well-suited for this esports project and simulated car training because it allows agents to learn efficient behaviors from interactions with the environment rather than requiring full programming of routes or actions.

1. NEERAJ BEDI
FOUNDER MADHAWK GAMES
Briefly About Reinforcement
Learning which we are using in
our Esports project?

2. Reinforcement learning learns by
interacting with its environment.
There are some key terms that
are important in Reinforcement
Learning

3. Like
Reward
Reward is An immediate return
given to an agent when agent
performs specific action or
assignment
Environment
Environment is just the Physical
world where the agent is
interacting

4. Policy
Policy is Method to map the
agent’s state to actions
Value
Value means rewards which we
receive after the agent takes
some action

5. Some Important Attribute of
Reinforcement Learning
1.
You work with reward signal it’s
either positive or negative
2.
Time plays a crucial role, and
you need to wait for Feedback

6. In robotics, Reinforcement
learning is used to enable the
robot to create an efficient
system for itself which learns
from its own experience and
behaviour
And Reinforcement learning can
be used in different things like
Gaming, Manufacturing, Delivery
Management healthcare and
online stock trading

7. now let's discuss how we are
using reinforcement learning in
esports
In our esports project we are
training NPC in every session,
The non-player character or NPC
receives rewards by performing
correctly and penalties for
performing incorrectly

8. We can use this technique is so
many other ways like if we have
an AI car in the game which
needs to go to destination
without crashing into trees, we
will use reinforcement learning
to train the car and you don’t
need to program full route

9. You just need to give positive
feedback and negative feedback
when it crashes into trees you
just need to give penalties, And
it will be an automatic process
and even if you replace with
different maps it will learn, so
this will be really efficient you
don’t need to program it again
and again

10. In the last When Not to Use
Reinforcement Learning?

11. When you have enough data to
solve the problem
You need to remember that
Reinforcement Learning needs
heavy resources,
So you need to analyse where
you can use reinforcement
learning