Reinforcement Learning at University Of Zurich | Flashcards & Summaries

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Lernmaterialien für Reinforcement Learning an der University of Zurich

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value function

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  • A value function estimates how good in terms of expected return it is to be in a certain state or to perform a certain action in a given state. 
  • Value functions are parametrized by the policy applied by the agent.
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Agent

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  • Learner and decision maker (excluding sensations and any “internal states”) 
  • The agent’s goal is to maximise the total reward it receives from the environment 
    • Deterministically or stochastically selects an action given a certain state of its environment
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Environment

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  • What the agent interacts with
    • includes everything “outside” the agent (including sensations and any “internal states”)
  • The information about the environment accessible to the agent at time t is encoded in a state variable St 
  • At each time step, the environment responds to the agent’s action by providing the agent with a reward.
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Reward

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  • Real valued number with negative rewards being interpreted as punishments. 
  • Given an action a applied in a given state s, the reward can be deterministic, or stochastic.
  • A reward is a real valued signal Rt ∈ ℛ ⊂ ℝ passing from the environment to the agent at each time step. 
  • The agent’s goal is to maximize (a monotonically increasing function of) the total amount of rewards it receives, not the immediate reward.
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Task

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  • Complete specification of an environment 
    • instance of the RL problem
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RL methods aim

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RL methods aim to maximize the expected return.

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Markov property

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A state signal that encodes all relevant information from past interactions with the environment (including past states, actions and received rewards)

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Markov Decision Process:

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A reinforcement learning task that fulfills the Markov property

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The Bellman Equations

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Express the recursive properties of value functions

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Bandit problems:

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Special case of the reinforcement learning problem: single state

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Exploration-Exploitation trade-off

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Whether it is better to explore or exploit depends on: 

  • Values and uncertainty of the estimates 
  • Number of remaining steps.
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Balancing exploitation and exploration

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  • Optimistic initial values method:
  • Upper-confidence-bound action selection method:
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  • 26911 Karteikarten
  • 534 Studierende
  • 19 Lernmaterialien

Beispielhafte Karteikarten für deinen Reinforcement Learning Kurs an der University of Zurich - von Kommilitonen auf StudySmarter erstellt!

Q:

value function

A:
  • A value function estimates how good in terms of expected return it is to be in a certain state or to perform a certain action in a given state. 
  • Value functions are parametrized by the policy applied by the agent.
Q:

Agent

A:
  • Learner and decision maker (excluding sensations and any “internal states”) 
  • The agent’s goal is to maximise the total reward it receives from the environment 
    • Deterministically or stochastically selects an action given a certain state of its environment
Q:

Environment

A:
  • What the agent interacts with
    • includes everything “outside” the agent (including sensations and any “internal states”)
  • The information about the environment accessible to the agent at time t is encoded in a state variable St 
  • At each time step, the environment responds to the agent’s action by providing the agent with a reward.
Q:

Reward

A:
  • Real valued number with negative rewards being interpreted as punishments. 
  • Given an action a applied in a given state s, the reward can be deterministic, or stochastic.
  • A reward is a real valued signal Rt ∈ ℛ ⊂ ℝ passing from the environment to the agent at each time step. 
  • The agent’s goal is to maximize (a monotonically increasing function of) the total amount of rewards it receives, not the immediate reward.
Q:

Task

A:
  • Complete specification of an environment 
    • instance of the RL problem
Mehr Karteikarten anzeigen
Q:

RL methods aim

A:

RL methods aim to maximize the expected return.

Q:

Markov property

A:

A state signal that encodes all relevant information from past interactions with the environment (including past states, actions and received rewards)

Q:

Markov Decision Process:

A:

A reinforcement learning task that fulfills the Markov property

Q:

The Bellman Equations

A:

Express the recursive properties of value functions

Q:

Bandit problems:

A:

Special case of the reinforcement learning problem: single state

Q:

Exploration-Exploitation trade-off

A:

Whether it is better to explore or exploit depends on: 

  • Values and uncertainty of the estimates 
  • Number of remaining steps.
Q:

Balancing exploitation and exploration

A:
  • Optimistic initial values method:
  • Upper-confidence-bound action selection method:
Reinforcement Learning

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