Engineering AI Agents
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Reinforcement Learning
Reinforcement Learning
Acting in environments with reward feedback
Author
Pantelis Monogioudis
Reinforcement Learning
Reinforcement Learning
Monte-Carlo Prediction
Temporal Difference (TD) Prediction
Generalized Policy Iteration
\(\epsilon\)
-greedy Monte-Carlo (MC) Control
The SARSA Algorithm
SARSA Gridworld Example
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(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
Different Approaches to solve known and unknown MDPs
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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