CODEWITHSUNDEEP
reinforcement-learning
Mastiff
Mastiff

Reputation: 2240

Which reinforcement learning algorithm is applicable to a problem with a continuously variable reward and no intermediate rewards?

I think the title says it. A "game" takes a number of moves to complete, at which point a total score is computed. The goal is to maximize this score, and there are no rewards provided for specific moves during the game. Is there an existing algorithm that is geared toward this type of problem?

EDIT: By "continuously variable" reward, I mean it is a floating point number, not a win/loss binary. So you can't, for example, respond to "winning" by reinforcing the moves made to get there. All you have is a number. You can rank different runs in order of preference, but a single result is not especially meaningful.

Upvotes: 1

Views: 90

Answers (1)

Pablo EM
Pablo EM

Reputation: 6669

First of all, in my opinion, the title of your question seems a little confusing when you talk about "continuously variable reward". Maybe you could clarify this aspect.

On the other hand, without taking into account the previous point, it looks your are talking about the temporal credit-assigment problem: How do you distribute credit for a sequence of actions which only obtain a reward (positive or negative) at the end of the sequence?

E.g., a Tic-tac-toe game where the agent doesn't recive any reward until the game ends. In this case, almost any RL algorithm tries to solve the temporal credit-assigment problem. See, for example, Section 1.5 of Sutton and Barto RL book, where they explain the working principles of RL and its advantages over other approaches using as example a Tic-tac-toe game.

Upvotes: 2

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