Skip to main content
13 votes
Accepted

Can reinforcement learning be used for tasks where only one final reward is received?

RL can be used for cases where you have sparse rewards (i.e. at almost every step all rewards are zero), but, in such a setting, the experience the agent receives during the trajectory does not ...
nbro's user avatar
  • 40.9k
5 votes
Accepted

How to improve the reward signal when the rewards are sparse?

Andrew Y. Ng (yes, that famous guy!) et al. proved, in the seminal paper Policy invariance under reward transformations: Theory and application to reward shaping (ICML, 1999), which was then part of ...
nbro's user avatar
  • 40.9k
2 votes

How to deal with the time delay in reinforcement learning?

This problem has been formally termed as Delayed MDP (Katsikopoulos & Engelbrecht, 2003)[1] - the actions generated are not instantly applied to the environment and/or the captured observations ...
mugoh's user avatar
  • 531
2 votes

How to deal with the time delay in reinforcement learning?

Most RL algorithms assume a discretization of time (although RL can also be applied to continuous-time problems [1]), i.e., in theory, it doesn't really matter what the actual time between consecutive ...
nbro's user avatar
  • 40.9k
2 votes

Reinforcement Learning with asynchronous feedback

I have been looking for a while into pretty much precisely the problem you describe (including the same application domain), but haven't been able to find much. The most obvious, mathematically "...
Dennis Soemers's user avatar
  • 10.3k

Only top scored, non community-wiki answers of a minimum length are eligible