Questions tagged [exploration-exploitation-tradeoff]

For questions related to the exploration-exploitation trade-off (or dilemma) in the context of reinforcement learning.

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Why is the ideal exploration parameter in the UCT algorithm $\sqrt{2}$?

From Wikipedia, in the Monte-Carlo Tree Search algorithm, you should choose the node that maximizes the value: $${\displaystyle {\frac {w_{i}}{n_{i}}}+c{\sqrt {\frac {\ln N_{i}}{n_{i}}}}},$$ where ${...
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What is the difference between exploitation and exploration in the context of optimization?

In the paper Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (2015, published in Knowledge-Based Systems) The test functions are divided to three groups: unimodal, multi-...
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Should the exploration rate be updated at the end of the episode or at every step?

My agent uses an $\epsilon$-greedy strategy to learn. The exploration rate (i.e. $\epsilon$) decays throughout the training. I've seen examples where people update $\epsilon$ every time an action is ...
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How to deal with the addition of a new state to the environment during training?

Let's say we have a dynamic environment: a new state gets added after 2000 episodes have been done. So, we leave room for exploration, so that it can discover the new state. When it gets to that new ...
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How can I increase the exploration in the Proximal Policy Optimation algorithm?

How can I increase the exploration in the Proximal Policy Optimation reinforcement learning algorithm? Is there a variable assigned for this purpose? I'm using the stable-baseline implementation: ...
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Why is the $\epsilon$ hyper-parameter (in the $\epsilon$-greedy policy) annealed smoothly?

As far as I understand, RL is a process that can be divided into 2 stages: Exploring a wide range of paths (acting randomly) Refining the current optimal paths (revolving around actions with a so-...
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PPO: sampling next action vs picking the most probable action

According to the original Proximal Policy Optimization paper (PPO paper), we always sample an action from the actor distribution. According to the link The overall loss is calculated as $\text{loss} =...
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How to modify the Actor-Critic policy gradient algorithm to perform Safe exploration in Reinforcement Learning

I am trying to implement safe exploration technique in [Ref.1]. I am using Soft Actor-Critic algorithm to teach an agent to introduce a bias between 0 and 1 to a specific state of interest in my ...