Planning under Uncertainty

Today’s Topics Sequential Decision Problems Markov Decision Process (MDP) Value Iteration Policy Iteration Partially Observable MDPs (POMDPs) Student Questions about the Midterm.

Big Assumption in Most of the Planning Techniques We’ve Seen so Far What is it? NO UNCERTAINTY! Assumes the agent knows everything about the world and what can happen in it. Sources of Uncertainty Agent may not know all states of the world. Agent may not know what state of the world it is in. Outcomes of actions may not be known

Sequential Decision Problem Example Beginning at the start state, choose an action at each time step. Problem terminates when either goal state is reached. Possible actions are Up, Down, Left, and Right Assume that the environment is fully observable, i.e., the agent always knows where it is.

Sequential Decision Problem Example Deterministic Solution: If the environment is deterministic and the objective is get the maximum reward  The solution is easy: (Up, Up, Right, Right, Right)

Sequential Decision Problem Example What if actions are unreliable? Suppose that there is a .8 probability to go to intended cell, but rest of the time it goes to cells at right angles of intended cell. If boundary or obstacle encountered, it does not move. The probability of reaching the goal state by executing (Up, Up, Right, Right, Right) is .85 + small probability of reaching the goal state the other path = .32776

Transition Model A transition model is a specification of the outcome probabilities for each action in each possible state. T(s,a,s¢) denotes the probability of reaching state s¢ if action a is done on state s. Make Markov Assumption, i.e., the probability of reaching state s¢ from s depends only on s and not on the history of earlier states.

Rewards and Utilities A utility function must be specified for the agent in order to determined the value of an action. Because the problem is sequential, the utility function depends on a sequence of states (environment history). Rewards are assigned to states, i.e., R(s) returns the reward of the state. For this example, assume the following: The reward for all states, except for the goal states, is -0.04. The utility function is the sum of all the states visited. E.g., if the agent reaches (4,3) in 10 steps, the total utility is 1 + (10 x -0.04) = 0.6. The negative reward is an incentive to stop interacting as quickly as possible.

Markov Decision Process (MDP) Specification for a sequential decision problem for a fully observable environment with a Markovian transition model and additive rewards. Three components: Initial State: S0 Transition Model: T(s,a,s¢) Reward Function: R(s)

Solution for an MDP Since outcomes of actions are not deterministic, a fixed set of actions cannot be a solution. A solution must specify what an a agent should do for any state that the agent might reach. A policy, denoted by π, recommends an action for a given state, i.e., π(s) is the action recommended by policy π for state s.

Quality of a Policy Since the environment is stochastic, each time a given policy is executed starting from the initial state, there can be different environment histories. Therefore, the quality of a policy is determined by the expected utility of the possible environment histories generated by that policy.

Optimal Policy An Optimal policy is a policy that yields the highest expected utility. Optimal policy is denoted by π*. Once a π* is computed for a problem, then the agent, once identifying the state (s) that it is in, consults π*(s) for the next action to execute.

Optimal Policy for Example Note that at (3,1), the policy goes back towards the initial state. Why?

Balancing Risk and Reward The balance of risk and reward depends on the value of R(s). Characteristic that appears often in the real world. MDPs have been studied in many fields (AI, OR, economics, control theory, etc.). The following four slides show π* for four different reward models.

R(s) < -1.6284 Get out of the environment as fast as possible.

-0.4278 < R(s) < -0.0850 Take the fastest route to (4,3) without concern for risk.

-0.0221 < R(s) < 0 Take no risks at all.

R(s) > 0 Never leave the environment.

Decision-Making Horizon Finite Horizon – Fixed time N after which nothing matters. Optimal action could change over time. E.g., in our example, suppose agent starts at (3,1) and N=3, then optimal action is to take the short cut. But, if N=100,… Optimal policy is nonstationary. Infinite Horizon – no fixed time and optimal action depends on the current state. Optimal policy is stationary.

Stationary Preferences between States Assumption about preferences remaining the same independent of time. If you prefer one future to another starting tomorrow, then you should still prefer that future if it were to start today. Given stationary preferences, there are two ways to assign utilities to sequences.

Assignment of Utility to State Sequences Utility function for environment histories (sequences of states) is denoted as Uh([S0,S1,…,Sn]). Two methods: Additive rewards – Sum up rewards of states, i.e., Uh([S0,S1,…]) = R(s0) + R(s1) + R(s2) + … Discounted Rewards – Sum of progressively discounted rewards of states, i.e., Uh([S0,S1,…]) = R(s0) + gR(s1) + g2R(s2) + …, where discount factor g is a number between 0 and 1. Closer g to 0, the less future rewards count. When g is 1, the same as Additive Rewards.

Issue with Calculating Utilities on Infinite Horizons If all environment histories are infinite (no terminal state reached), using additive rewards results in comparing +∞. 3 Solutions Discounted rewards – if rewards bounded by Rmax and g < 1, then Uh([S0,S1,…]) ≤ Rmax/(1 - g). Ensure Proper policy, i.e., policy that is guaranteed to reach a terminal state. Compare in terms of average reward (difficult to analyze).

Choosing between Policies The value of a policy is the expected sum of discounted rewards obtained, where the expectation is taken over all possible state sequences that could occur, give that the policy is executed.

Value Iteration Value Iteration is an algorithm for computing an optimal policy. Basic idea: Calculate the utility of each state and then use the state utilities to select an optimal action in each state.

Utility of States Utility of a state is the expected utility of the state sequences that might follow it, which are determined by a policy. Let Uπ(s) be the utility of a state and st be the state the agent is in after executing π for t steps, then Let U(s) be a shorthand for Uπ*(s)

Utilities for Example Problem Note that utilities closer to (4,3) are higher because fewer steps are required to reach the exit.

Bellman Equation π* selects the action that maximizes the expected utility of the subsequent state. The Bellman equation defines U(s) as the utility of s plus the discounted utility of the next state, assuming the optimal action, i.e.,

Computing Bellman equation on Example Problem The equation for state (1,1) is When we plug in the numbers from slide 26, we find that Up is the best action.

Using Bellman equations for solving MDPs. If there are n possible states, then there are n Bellman equations (one for each state). To compute the n utilities, we would like to solve simultaneously the n Bellman equations. Problematic because max is not a linear operator. Use iteration applying Bellman update: Start with the utilities of all states initialized to 0. Guaranteed to converge.

Value-Iteration Algorithm

Value-Iteration Convergence Evolution of selected states using value iteration. Note that some states have negative reward and until +1 goal state utility propagation reaches them The number of iterations required to guarantee an error of at most e = c x Rmax, for different values of c,, as a function of the discount factorg.

Are True Utilities for States Required? What matters is that utilities are good enough to recommend the optimal action in each state. In practice πi often becomes optimal before Ui has converged. For our example, the policy πi is optimal when i = 4 even though the maximum error in Ui is still 0.46

Policy Iteration Searches policy space. Basic idea: Policy Evaluation: start with a random policy π0 and calculate utilities based on if that policy were executed. Policy Improvement: Calculate a new MEU policy πi+1 based on computed utilities. Iterate until the policy does not change.

Policy-Iteration Algorithm

Policy Evaluation Because policies are fixed, the max operator is removed and standard linear algebra methods can applied to solve simultaneous equations. Complexity is O(n3) For large state spaces, it may be prohibitive. Modified Policy Iteration - can do some number of value iterations (simplified because policy is fixed) to get reasonable approximation of utilities.

Partially Observability What do you do if the system state cannot always be determined? Action outcomes are not fully observable. Use a Partially Observable MDP (POMDP). Must add: a set of observations O to the model an observation distribution U(s,o) for each state. an initial state distribution.

POMDP Basic decision cycle: Given the current belief state b, execute the action a = π*(b). Receive the observation Update current belief state based on previous belief state, the action taken, and the new observation. Solve as an MDP by reasoning in belief space Requires calculating a probability distribution over the possible states given previous observations.

Big Problem with MDPs and Variants Does not scale. Too many states in real-world problems. There are methods for focusing search only on significant states. What if outcome is not in transition model? Have been attempts to have hybrid approaches with MDP for short horizon and estimates through heuristic search for utilities for distant states.