Questions tagged [sparse-rewards]

For questions about the sparsity of the rewards (or reward function), which can slow down learning. Reward shaping can be used to solve this problem.

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How do I compute the value function when the reward is only at the end in the context of actor-critic algorithms?

Consider the actor-critic reinforcement learning setting (actor and critic parameterized by a neural network). The reward is given only at the end of the episode (or when there is a timeout there is ...
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5 votes
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How to improve the reward signal when the rewards are sparse?

In cases where the reward is delayed, this can negatively impact a models ability to do proper credit assignment. In the case of a sparse reward, are there ways in which this can be negated? In a ...
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How to apply Q-learning when rewards is only available at the last state?

I have a scheduling problem in which there are $n$ slots and $m$ clients. I am trying to solve the problem using Q-learning so I have made the following state-action model. A state $s_t$ is given by ...
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What are the pros and cons of sparse and dense rewards in reinforcement learning?

From what I understand, if the rewards are sparse the agent will have to explore more to get rewards and learn the optimal policy, whereas if the rewards are dense in time, the agent is quickly guided ...
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4 votes
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How does the optimization process in hindsight experience replay exactly work?

I was reading the following research paper Hindsight Experience Replay. This is the paper that introduces a concept called Hindsight Experience Replay (HER), which basically attempts to alleviate the ...
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3 votes
1 answer
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Are there any reliable ways of modifying the reward function to make the rewards less sparse?

If I am training an agent to try and navigate a maze as fast as possible, a simple reward would be something like \begin{align} R(\text{terminal}) &= N - \text{time}\ \ , \ \ N \gg \text{...
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2 votes
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Can reinforcement learning be used for tasks where only one final reward is received?

Is reinforcement learning problem adaptable to the setting when there is only one - final - reward. I am aware of problems with sparse and delayed rewards, but what about only one reward and a quite ...
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