Engineering AI Agents
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Foundations
Training Deep Networks
Perception
State Estimation
Large Language Models
Multimodal Reasoning
Planning
Markov Decision Processes
Reinforcement Learning
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AI for Robotics
Deep Learning for Computer Vision
DATA MINING - BEING PORTED
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Reinforcement Learning
Reinforcement Learning
Acting in environments with reward feedback
Author
Pantelis Monogioudis
Reinforcement Learning
Reinforcement Learning
Generalized Policy Iteration
Monte-Carlo Prediction
Temporal Difference (TD) Prediction
MC vs. TD(0)
\(\epsilon\)
-greedy Monte-Carlo (MC) Control
The SARSA Algorithm
SARSA Gridworld Example
Categories
All
(7)
\(\epsilon\)
-greedy Monte-Carlo (MC) Control
In this section we outline methods that can result in optimal policies when the MDP is
unknown
and we need to
learn
its underlying functions / models - also known as the
mode…
Generalized Policy Iteration
As we saw in the dynamic programming (DP) solution MDP problem, policy iteration is an algorithm that consists of two simultaneous, interacting processes: one making the…
Monte-Carlo Prediction
In this chapter we find optimal policy solutions when the MDP is
unknown
and we need to
learn
its underlying value functions - also known as the
model free
prediction…
Reinforcement Learning
We started looking at different agent behavior architectures starting from the planning agents where the
model
of the environment is known and with
no interaction
with it…
Temporal Difference (TD) Prediction
If one had to identify one idea as central and novel to reinforcement learning, it would undoubtedly be temporal-difference(TD) learning. TD learning is a combination of…
The SARSA Algorithm
SARSA implements a
\(Q(s,a)\)
value-based GPI and naturally follows as an enhancement from the
\(\epsilon-greedy\)
policy improvement step of MC control.
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