# Does eligibility traces and epsilon-greedy do the same task in different ways?

I understand that, in Reinforcement Learning algorithms, such as Q-learning, to prevent selecting the actions with greatest q-values too fast and allow for exploration, we use eligibility traces.

Here are some questions

1. Does $$\epsilon$$-greedy solve the same problem?

2. Do these two approaches aim to attain the same objective?

3. What are the advantages of each over the other?

Eligibility traces attempt to solve a different problem. Their function is to keep a short term memory of what states have been recently visited. They unify and generalize TD and Monte Carlo methods producing a family of methods spanning a spectrum that has Monte Carlo methods at one end ($$\lambda = 1$$) and one-step TD methods at the other ($$\lambda = 0$$). Using traces, one can keep the features of previous states around, but at faded values depending on the choice of $$\lambda$$. Choosing a low $$\gamma$$ (near $$0$$) makes the traces myopic in that the traces quickly go to $$0$$. If a larger $$\lambda$$ is used (near $$1$$) the traces stay around for much longer and can help an agent determine the difference between what would otherwise be 2 identical states.
For more information, check out Reinforcement Learning: An Introduction, chapter 2 (page 30 introduces $$\epsilon$$-greedy methods) and chapter 12 (eligibility traces).