Atomic Model for stochastic environments with generalized rewards Deterministic worlds + goals of attainment


НазваAtomic Model for stochastic environments with generalized rewards Deterministic worlds + goals of attainment
Дата конвертації26.04.2013
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ТипПрезентации



Atomic Model for stochastic environments with generalized rewards

Deterministic worlds + goals of attainment



Optimal Policies depend on horizon, rewards..



Classical Planning Assumptions



Stochastic/Probabilistic Planning: Markov Decision Process (MDP) Model



Types of Uncertainty

  • Disjunctive (used by non-deterministic planning)

Next state could be one of a set of states.
  • Stochastic/Probabilistic

Next state is drawn from a probability distribution over the set of states.

How are these models related?

Markov Decision Processes

  • An MDP has four components: S, A, R, T:

    • (finite) state set S (|S| = n)
    • (finite) action set A (|A| = m)
    • (Markov) transition function T(s,a,s’) = Pr(s’ | s,a)
    • Probability of going to state s’ after taking action a in state s
    • How many parameters does it take to represent?
    • bounded, real-valued (Markov) reward function R(s)
    • Immediate reward we get for being in state s
    • For example in a goal-based domain R(s) may equal 1 for goal states and 0 for all others
    • Can be generalized to include action costs: R(s,a)
    • Can be generalized to be a stochastic function
  • Can easily generalize to countable or continuous state and action spaces (but algorithms will be different)



Graphical View of MDP



Assumptions

  • First-Order Markovian dynamics (history independence)

    • Pr(St+1|At,St,At-1,St-1,..., S0) = Pr(St+1|At,St)
    • Next state only depends on current state and current action
  • First-Order Markovian reward process

    • Pr(Rt|At,St,At-1,St-1,..., S0) = Pr(Rt|At,St)
    • Reward only depends on current state and action
    • As described earlier we will assume reward is specified by a deterministic function R(s)
    • i.e. Pr(Rt=R(St) | At,St) = 1
  • Stationary dynamics and reward

    • Pr(St+1|At,St) = Pr(Sk+1|Ak,Sk) for all t, k
    • The world dynamics do not depend on the absolute time
  • Full observability

    • Though we can’t predict exactly which state we will reach when we execute an action, once it is realized, we know what it is


Policies (“plans” for MDPs)

  • Nonstationary policy [Even though we have stationary dynamics and reward??]

    • π:S x T → A, where T is the non-negative integers
    • π(s,t) is action to do at state s with t stages-to-go
    • What if we want to keep acting indefinitely?
  • Stationary policy

    • π:S → A
    • π(s) is action to do at state s (regardless of time)
    • specifies a continuously reactive controller
  • These assume or have these properties:

    • full observability
    • history-independence
    • deterministic action choice


Value of a Policy

  • How good is a policy π?

  • How do we measure “accumulated” reward?

  • Value function V: S →ℝ associates value with each state (or each state and time for non-stationary π)

  • Vπ(s) denotes value of policy at state s

    • Depends on immediate reward, but also what you achieve subsequently by following π
    • An optimal policy is one that is no worse than any other policy at any state
  • The goal of MDP planning is to compute an optimal policy (method depends on how we define value)



Finite-Horizon Value Functions

  • We first consider maximizing total reward over a finite horizon

  • Assumes the agent has n time steps to live

  • To act optimally, should the agent use a stationary or non-stationary policy?

  • Put another way:

    • If you had only one week to live would you act the same way as if you had fifty years to live?


Finite Horizon Problems

  • Value (utility) depends on stage-to-go

    • hence so should policy: nonstationary π(s,k)
  • is k-stage-to-go value function for π

    • expected total reward after executing π for k time steps (for k=0?)
  • Here Rt and st are random variables denoting the reward received and state at stage t respectively



Computing Finite-Horizon Value

  • Can use dynamic programming to compute

    • Markov property is critical for this

(a)

(b)

Bellman Backup



Value Iteration: Finite Horizon Case

  • Markov property allows exploitation of DP principle for optimal policy construction

    • no need to enumerate |A|Tn possible policies
  • Value Iteration



Value Iteration



Value Iteration



Value Iteration

  • Note how DP is used

    • optimal soln to k-1 stage problem can be used without modification as part of optimal soln to k-stage problem
  • Because of finite horizon, policy nonstationary

  • What is the computational complexity?

    • T iterations
    • At each iteration, each of n states, computes expectation for |A| actions
    • Each expectation takes O(n) time
  • Total time complexity: O(T|A|n2)

    • Polynomial in number of states. Is this good?


Summary: Finite Horizon

  • Resulting policy is optimal

    • convince yourself of this
  • Note: optimal value function is unique, but optimal policy is not

    • Many policies can have same value


Discounted Infinite Horizon MDPs

  • Defining value as total reward is problematic with infinite horizons

    • many or all policies have infinite expected reward
    • some MDPs are ok (e.g., zero-cost absorbing states)
  • “Trick”: introduce discount factor 0 ≤ β < 1

    • future rewards discounted by β per time step
  • Note:

  • Motivation: economic? failure prob? convenience?



Notes: Discounted Infinite Horizon

  • Optimal policy maximizes value at each state

  • Optimal policies guaranteed to exist (Howard60)

  • Can restrict attention to stationary policies

    • I.e. there is always an optimal stationary policy
    • Why change action at state s at new time t?
  • We define for some optimal π



Policy Evaluation

  • Value equation for fixed policy

  • How can we compute the value function for a policy?

    • we are given R and Pr
    • simple linear system with n variables (each variables is value of a state) and n constraints (one value equation for each state)
    • Use linear algebra (e.g. matrix inverse)


Computing an Optimal Value Function

  • Bellman equation for optimal value function

    • Bellman proved this is always true
  • How can we compute the optimal value function?

    • The MAX operator makes the system non-linear, so the problem is more difficult than policy evaluation
  • Notice that the optimal value function is a fixed-point of the Bellman Backup operator B

    • B takes a value function as input and returns a new value function


Value Iteration

  • Can compute optimal policy using value iteration, just like finite-horizon problems (just include discount term)

  • Will converge to the optimal value function as k gets large. Why?



Convergence

  • B[V] is a contraction operator on value functions

    • For any V and V’ we have || B[V] – B[V’] || ≤ β || V – V’ ||
    • Here ||V|| is the max-norm, which returns the maximum element of the vector
    • So applying a Bellman backup to any two value functions causes them to get closer together in the max-norm sense.
  • Convergence is assured

    • any V: || V* - B[V] || = || B[V*] – B[V] || ≤ β|| V* - V ||
    • so applying Bellman backup to any value function brings us closer to V* by a factor β
    • thus, Bellman fixed point theorems ensure convergence in the limit
  • When to stop value iteration? when ||Vk - Vk-1||≤ ε

    • this ensures ||Vk – V*|| ≤ εβ /1-β


Contraction property proof sketch

  • Note that for any functions f and g

  • We can use this to show that

    • |B[V]-B[V’]| <= b|V – V’|


How to Act

  • Given a Vk from value iteration that closely approximates V*, what should we use as our policy?

  • Use greedy policy:

  • Note that the value of greedy policy may not be equal to Vk

  • Let VG be the value of the greedy policy? How close is VG to V*?



How to Act

  • Given a Vk from value iteration that closely approximates V*, what should we use as our policy?

  • Use greedy policy:

    • We can show that greedy is not too far from optimal if Vk is close to V*
  • In particular, if Vk is within ε of V*, then VG within 2εβ /1-β of V* (if ε is 0.001 and β is 0.9, we have 0.018)

  • Furthermore, there exists a finite ε s.t. greedy policy is optimal

    • That is, even if value estimate is off, greedy policy is optimal once it is close enough


Policy Iteration

  • Given fixed policy, can compute its value exactly:

  • Policy iteration exploits this: iterates steps of policy evaluation and policy improvement



Policy Iteration Notes

  • Each step of policy iteration is guaranteed to strictly improve the policy at some state when improvement is possible

  • Convergence assured (Howard)

    • intuitively: no local maxima in value space, and each policy must improve value; since finite number of policies, will converge to optimal policy
  • Gives exact value of optimal policy



Value Iteration vs. Policy Iteration

  • Which is faster? VI or PI

    • It depends on the problem
  • VI takes more iterations than PI, but PI requires more time on each iteration

    • PI must perform policy evaluation on each step which involves solving a linear system
    • Also, VI can be done with asynchronous and prioritized update fashion..
  • Complexity:

    • There are at most exp(n) policies, so PI is no worse than exponential time in number of states
    • Empirically O(n) iterations are required
    • Still no polynomial bound on the number of PI iterations (open problem)!


Markov Decision Process (MDP)

  • S: A set of states

  • A: A set of actions

  • Pr(s’|s,a): transition model

    • (aka Mas,s’)
  • C(s,a,s’): cost model

  • G: set of goals

  • s0: start state

  • : discount factor

  • R(s,a,s’): reward model



Examples of MDPs

  • Goal-directed, Indefinite Horizon, Cost Minimization MDP

    • <S, A, Pr, C, G, s0>
    • Most often studied in planning community
  • Infinite Horizon, Discounted Reward Maximization MDP

    • <S, A, Pr, R, >
    • Most often studied in reinforcement learning
  • Goal-directed, Finite Horizon, Prob. Maximization MDP

    • <S, A, Pr, G, s0, T>
    • Also studied in planning community
  • Oversubscription Planning: Non absorbing goals, Reward Max. MDP

    • <S, A, Pr, G, R, s0>
    • Relatively recent model


SSPP—Stochastic Shortest Path Problem An MDP with Init and Goal states

  • MDPs don’t have a notion of an “initial” and “goal” state. (Process orientation instead of “task” orientation)

    • Goals are sort of modeled by reward functions
    • Allows pretty expressive goals (in theory)
    • Normal MDP algorithms don’t use initial state information (since policy is supposed to cover the entire search space anyway).
    • Could consider “envelope extension” methods
      • Compute a “deterministic” plan (which gives the policy for some of the states; Extend the policy to other states that are likely to happen during execution
      • RTDP methods


Bellman Equations for Cost Minimization MDP (absorbing goals)[also called Stochastic Shortest Path]

  • <S, A, Pr, C, G, s0>

  • Define J*(s) {optimal cost} as the minimum expected cost to reach a goal from this state.

  • J* should satisfy the following equation:



Bellman Equations for infinite horizon discounted reward maximization MDP

  • <S, A, Pr, R, s0, >

  • Define V*(s) {optimal value} as the maximum expected discounted reward from this state.

  • V* should satisfy the following equation:



Bellman Equations for probability maximization MDP

  • <S, A, Pr, G, s0, T>

  • Define P*(s,t) {optimal prob.} as the maximum probability of reaching a goal from this state at tth timestep.

  • P* should satisfy the following equation:



Modeling Softgoal problems as deterministic MDPs

  • Consider the net-benefit problem, where actions have costs, and goals have utilities, and we want a plan with the highest net benefit

  • How do we model this as MDP?

    • (wrong idea): Make every state in which any subset of goals hold into a sink state with reward equal to the cumulative sum of utilities of the goals.
    • Problem—what if achieving g1 g2 will necessarily lead you through a state where g1 is already true?
    • (correct version): Make a new fluent called “done” dummy action called Done-Deal. It is applicable in any state and asserts the fluent “done”. All “done” states are sink states. Their reward is equal to sum of rewards of the individual states.


Heuristic Search vs. Dynamic Programming (Value/Policy Iteration)

  • VI and PI approaches use Dynamic Programming Update

  • Set the value of a state in terms of the maximum expected value achievable by doing actions from that state.

  • They do the update for every state in the state space

    • Wasteful if we know the initial state(s) that the agent is starting from


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