RL is mapping of X to Y in order to maximise Z. What are X, Y & Z?
Actions, situations, reward signal
What does RL explicitly consider?
The whole problem of a goal-directed agent interacting with an unknown environment
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What is a less formal way of describing RL?
Learning through trial & error
In RL, what are the two types of feedback the environment provides the agent?
State & state evaluation (reward)
In RL, what signal does the agent send to the environment?
Action
In RL what does the reward signal communicate?
What is required, not how to do it
In RL what does the reward signal tell the agent about the action's correctness?
Does not explicitly indicate whether action was correct or incorrect
What are the 3 main differences in supervised learning vs reinforced learning?
1.) The system learns from examples by a knowledgeable external factor 2.) Environment explicitly indicates what the agent's action should have been 3.) Instructive feedback independent of output
What are the 2 main situations where a RL system of learning is appropriate?
1.) When it is impossible to get sufficient examples of desired behaviour 2.) Learning from experience becomes more appropriate when it becomes difficult for the examples of desired behaviour correct and representative of all situations the agent is likely to experience
What are the 3 main components of an RL algorithm?
1.) Reward function 2.) Value function 3.) Policy
What is the "reward function"?
Function that defines a goal by specifying a number for each state-action combination
What is the "value function"?
Function that specifies total reward expected when starting from a given state with a given behaviour
What is the "policy"?
Mapping from perceived state to action
What is "discounting"?
Reward is discounted over longer runs according to some discount rate with range 0 - 1
What is the discounted return R at time t (discount function)? (PHOTO)
R▼t = r▼t+1 + xr▼t+2 + xr▼t+3...
= Σx^nr▼t+n
What is the action value estimation function (for small k values where maintaining a prior reward list is feasible)? (PHOTO)
Q▼t(a) = (r▼1 + r▼2... + r▼k) / k
What is the action value estimation function (for large k values where maintaining a prior reward list is impossible)? (PHOTO)
Q▼k+1 = Q▼k + 1/(k+1) x (r▼k+1 - Q▼k)
or replace 1/(k+1) with a constant for dynamic tasks
Describe a greedy reward policy
Highest action value used to select output for given situation
Name 2 other reward policies besides "greedy"
ε-greedy, annealed
What two things does an environment with the Markov Property allow us to predict?
Its one-step dynamics enable next-state predictions & expected next reward
Compare policy for a Markov state vs. policy as a function of complete histories
They are the same
Explain partially-observable states (the perceptual aliasing problem)
If an entity's inputs convey partial information about the environment, there may be situations which appear identical to the agent but require different optimal actions
SARSA PSEUDOCODE PHOTO
SARSA PSEUDOCODE PHOTO
Explain "on-policy"
An on-policy algorithm evaluates the policy actually used
Explain "off-policy"
An off-policy algorithm approximates optimal action-value function independently of the policy being followed
ONE-STEP Q-LEARNING FORMULA PHOTO
ONE-STEP Q-LEARNING FORMULA PHOTO
What are the 6 main steps in Q-Learning Pseudocode (PHOTO)
1.) Initialise action value function for states s and actions a Q(s, a) 2.) For each episode... Initialise s 3.) For each step of trial... Choose a from s via policy derived from Q 4.) Take action a, observe r, s' 5.) Update Q(s, a), 6.) s < -s' END foreach END foreach UNTIL terminal
Why is creating a table value for every state-action mapping not feasible in some problems?
In complex problems, the data memory required or time to visit all combinations would be too large
How do you generalise in complex problems?
Approximate for states not experienced, through supervised learning
What are two ways you can approximate functions during generalisation of complex problems?
Gradient descent, MLPs
Briefly describe how you would use an MLP to approximate functions in complex problem generalisation
Use on MLP per action, use the state as the input, MLP returns Q(s, a) per step
Give two applications of RL in game-playing
Backgammon, draughts
Give two applications of RL in engineering
Robotics, lift-allocation
Give two applications on RL in software
Adaptive games, browser agents