Kunstmatige Intelligentie / RuG KI Reinforcement Learning Sander van Dijk

What is Learning ? Percepts received by an agent should be used not only for acting, but also for improving the agent’s ability to behave optimally in the future to achieve its goal. Interaction between an agent and the world

Learning Types Supervised learning: Input, output) pairs of the function to be learned can be perceived or are given. Back-propagation Unsupervised Learning: No information at all about given output SOM Reinforcement learning: Agent receives no examples and starts with no model of the environment and no utility function. Agent gets feedback through rewards, or reinforcement.

Reinforcement Learning Task Learn how to behave successfully to achieve a goal while interacting with an external environment Learn through experience from trial and error Examples Game playing: The agent knows it has won or lost, but it doesn’t know the appropriate action in each state Control: a traffic system can measure the delay of cars, but not know how to decrease it.

Elements of RL Transition model, how action influence states Reward R, immediate value of state-action transition Policy , maps states to actions Agent Environment StateRewardAction Policy

Elements of RL r(state, action) immediate reward values G

Elements of RL Value function: maps states to state values Discount factor   [0, 1) (here 0.9) V * (state) values r(state, action) immediate reward values G G   2 11 π trγ t γrtrsV... G G

RL task (restated) Execute actions in environment, observe results. Learn action policy  : state  action that maximizes expected discounted reward E [r(t) +  r(t + 1) +  2 r(t + 2) + …] from any starting state in S

Reinforcement Learning Target function is  : state  action However… We have no training examples of form Training examples are of form, reward>

Utility-based agents Try to learn V  * (abbreviated V*) Perform look ahead search to choose best action from any state s Works well if agent knows  : state  action  state r : state  action  R When agent doesn’t know  and r, cannot choose actions this way

Q-values Define new function very similar to V* If agent learns Q, it can choose optimal action even without knowing  or R Using Q

Learning the Q-value Note: Q and V* closely related Allows us to write Q recursively as Temporal Difference learning

Learning the Q-value FOR each DO Initialize table entry: Observe current state s WHILE (true) DO Select action a and execute it Receive immediate reward r Observe new state s’ Update table entry for as follows Move: record transition from s to s’

r(state, action) immediate reward values Q(state, action) values V * (state) values G G G Q-learning Q-learning, learns the expected utility of taking a particular action a in a particular state s (Q-value of the pair (s,a))

Representation Explicit Implicit Weighted linear function/neural network Classical weight updating StateActionQ(s, a) 2MoveLeft81 2MoveRight100...

Exploration Agent follows policy deduced from learned Q-values Agent always performs same action in certain state, but perhaps there is an even better action? Exploration: Be safe learn more, greed curiosity. Extremely hard, if not impossible, to obtain optimal exploration policy. Randomly try actions that have not been tried often before but avoid actions that are believed to be of low utility

Q-learning estimates one time step difference Why not for n steps? Enhancement: Q( )

Q( ) formula Intuitive idea: use constant 0   1 to combine estimates from various look ahead distances (note normalization factor (1- )) Enhancement: Q( )

Enhancement: Eligibility Traces Look backward instead of forward. Weigh updates by eligibility trace e(s, a). On each step, decay all traces by  and increment the trace for the current state- action pair by 1. Update all state-action pairs in proportion to their eligibility.

Genetic algorithms Imagine the individuals as agent functions Fitness function as performance measure or reward function No attempt made to learn the relationship between the rewards and actions taken by an agent Simply searches directly in the individual space to find one that maximizes the fitness functions

Genetic algorithms Represent an individual as a binary string Selection works like this: if individual X scores twice as high as Y on the fitness function, then X is twice as likely to be selected for reproduction than Y. Reproduction is accomplished by cross-over and mutation

Cart – Pole balancing Demonstration

Summary RL addresses the problem of learning control strategies for autonomous agents TD-algorithms learn by iteratively reducing the differences between the estimates produced by the agent at different times In Q-learning an evaluation function over states and actions is learned In the genetic approach, the relation between rewards and actions is not learned. You simply search the fitness function space.