Reinforcement Learning and the Reward Engineering Principle
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Transcript of Reinforcement Learning and the Reward Engineering Principle
Reinforcement Learning and the Reward Engineering Principle
Daniel Dewey
[email protected]; AAAI Spring Symposium Series 2014
A modest aim:
What role goals in AI research?
…through the lens of reinforcement
learning.
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Reinforcement learning and AI
Definitions: “control” “dominance”
The reward engineering principle
Conclusions
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Stuart Russell, “Rationality and Intelligence”
RL and AI
“…one can define AI as the problem of designing systems that do the right thing.
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Now we just need a definition for
‘right.’”
Reinforcement learning provides a definition: maximize total rewards.
RL and AI
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action
reward
state
Agent EnvironmentAI
RL and AI
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Understand and Exploit
Inference, Planning, Learning,
Metareasoning, Concept formation,
etc…
RL and AI
Advantages:• Simple and cheap• Flexible and abstract• Measurable
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“worse is better”
…and used in natural neural nets (brains!)
RL and AI
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Outside the frame:Some behaviours cannot be elicited(by any rewards!)
As RL AI becomes more general and autonomous, it becomes harder to get good results with RL.
Key concepts: Control and dominance
Reinforcement learning and AI
Definitions: “control” “dominance”
The reward engineering principle
Conclusions
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Definitions: “control”
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A user has control when the agent’s received rewards equal the user’s chosen reward.
Definitions: “control”
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action
reward
state
Agent Environment
Definitions: “control”
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action
reward
Environment 1
User
Environment 2
state action
reward
Definitions: “control”
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user chooses reward
Environment 2
Agent User
Environment 1
Definitions: “control”
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Agent
env. “chooses” reward
Environment 2
Environment 1
User
Definitions: “dominance”
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Why does control matter?
Loss of control can create situations where no possible sequence of rewards can elicit the desired behaviour.
These behaviours are dominated by other behaviours.
Definitions: “dominance”
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A “behaviour” (sequence of actions) is a policy.
1 ? 0 ? ? ? 0 ?
a1 a2 a3 a7a4 a5 a6 a8
P1
Definitions: “dominance”
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1 ? 0 ? ? ? 0 ?P1
User-chosen rewards
Definitions: “dominance”
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Env.-chosen rewards (loss of control)
1 ? 0 ? ? ? 0 ?P1
Definitions: “dominance”
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1 ? 0 ? ? ? 0 ?P1
1 0 ? 1 ? ? 1 1P2
Can rewards make either better?
Definitions: “dominance”
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1 1 0 1 1 1 0 1P1
1 0 0 1 0 0 1 1P2
Choose all rewards 1: Max. reward = 6
Choose all rewards 0: Min. reward = 4
Definitions: “dominance”
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1 0 0 0 0 0 0 0P1
1 0 1 1 1 1 1 1P2
Choose all rewards 0: Min. reward = 1
Choose all rewards 1: Max. reward = 7
Definitions: “dominance”
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1 ? 0 ? ? ? 0 ?P1
1 1 1 1 1 ? 1 1P3
Definitions: “dominance”
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1 1 0 1 1 1 0 1P1
1 1 1 1 1 0 1 1P3
Max. reward = 6
Min. reward = 7
Definitions: “dominance”
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Dominated by P3
Dominates P1
1 ? 0 ? ? ? 0 ?P1
1 1 1 1 1 ? 1 1P3
Definitions: “dominance”
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A dominates B if no possible assignment of rewards causes R(A) > R(B).
No series of rewards can prompt a dominated policy; they are unelicitable. (A less obvious
result: every unelicitable policy is dominated.)
Recap
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Control is sometimes lost;
Loss of control enables dominance;
Dominance makes some policies
unelicitable.
All of this is outside the “RL AI
frame”
…but is clearly part of the AI problem(do the right thing!)
Generality: the range of policies an agent has reasonably efficient access to.
Autonomy: ability to function in environments with little interaction from users.
= better chance of finding dominant policies
= more frequent loss of control
Additional factors
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Reinforcement learning and AI
Definitions: “control” “dominance”
The reward engineering principle
Conclusions
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Reward Engineering Principle
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As RL AI becomes more general and autonomous, it becomes both more difficult and more important to constrain the environment to avoid loss of control.…because general / autonomous RL AI has• better chance of dominant policies;• more unelicitable policies;• more significant effects
Reinforcement learning and AI
Definitions: “control” “dominance”
The reward engineering principle
Conclusions
[email protected]; AAAI Spring Symposium Series 2014
[email protected]; AAAI Spring Symposium Series 2014
Heed the Reward Engineering Principle.
• Consider existence of dominant policies
• Be as rigorous as possible in excluding them
• Remember what’s outside the frame!
RL AI users:
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Expand the frame! Make goal design a first-class citizen.
Consider alternatives: manually coded utility functions, preference learning, …?
Watch out for dominance relations (e.g. in “dual” motivation systems, between intrinsic and extrinsic)
AI Researchers:
Thank you!
Work supported by theAlexander Tamas Research Fellowship
[email protected]; AAAI Spring Symposium Series 2014
Toby Ord, Seán Ó hÉigeartaigh, and two anonymous judges, for comments.