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• David Silver's Course Slides: '15
  
  • 01 - Introduction (slides, video)
  • 02 - Markov Decision Processes (slides, video)
  • 03 - Dynamic Programming (slides, video)
  • 04 - Model Free Prediction (slides, video)
  • 05 - Model Free Control (slides, video)

Prediction

These methods estimate the value function of an unknown MDP.

Monte Carlo

• Learns directly from complete episodes of experience. Caveat: all episodes must terminate
• Uses the concept 'value = empirical mean return'
• Goal is to learn

  Mathinline
  host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body v_{\pi}

   from episodes of experience under policy

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  host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body \pi

• Challenges:
  • Make sure we visit all states over our episodes enough times sufficient for a mean to be approaching a limit

Method

• Gather a set of episodes and run over each of them
  • Each time a state is visited:
    • Increment a counter 

      Mathinline
      host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body N(s) \leftarrow N(s) + 1

    • Increment the total return for the state 

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      host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body S(s) \leftarrow S(s) + G_t

• Value is estimated by the mean return 

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  host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body V(s) = S(s) / N(s)

  • Often faster to implement with incremental means
• By the law of large numbers 

  Mathinline
  host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body V(s) \rightarrow v_{\pi}(s)

   as 

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  host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body N(s) \rightarrow \infty

The increments can be done on first visits only, or on every visit and the limiting results will still hold. Why one or the other?

Typically the value function is estimated and updated on the fly with incremental mean calculations:

V(S_t) \leftarrow V(S_t) + \frac{1}{N(S_t)} ( G_t - V(S_t))

or  over a moving window (slowly forget old episode contributions):

V(S_t) \leftarrow V(S_t) + \alpha ( G_t - V(S_t))

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• Greedy in the Limit with Infinite Exploration (GLIE)
  • All state-action pairs are explored infinitely many times
  • The policy converges ona greedy policy
• Guarantee GLIE
  • Evaluation: Something to do with sampling and counting?
  • Improvement: Make sure epsilon goes to zero as the update count goes up

On-Policy TD (Sarsa)

• Convergence - guaranteed if policies are GLIE and step sizes satisfy the Robbins-Munro properties for a sequence
  • Robbins-Munro - 

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    host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body \sum_i \alpha_i = \infty

    , 

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    host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body \sum_i \alpha_i^2 < \infty

• Can consider n-step returns as we did for TD prediction, what is the best n to use? Sarsa(lambda)! Also can utilise eligibility traces

The lambda approach can help you update more quickly over one episode. See below.

Off-Policy Learning

Goal

• Evaluate target policy 

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  host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body \pi(a|s)

   to compute 

  Mathinline
  host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body v_{\pi}(s)

   or 

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  host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body q_{\pi}(s,a)

   while following behaviour policy 

  Mathinline
  host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body \mu(a|s)

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• Importance Sampling - has high variance (noise) for Monte-Carlo, but can be made to work for TD.
• Target Policy

  Mathinline
  host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body \pi

   - is the policy we are trying to optimise
• Behaviour Policy

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  host 5cf3c9eb-f97f-3ae9-acb7-6704dfd8f9e4 body \mu

   - is the policy we are trying to improve

Off-Policy Q-Learning

Take the next step from the observed policy, but then consider bootstrapping the rest from the optimal policy

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