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Hierarchical reinforcement learning exploration methods

One of the extensions to hierarchical reinforcement learning is change the degree of randomness that is applied while choosing an action. It is possible to increase the randomness to encourage ...
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How does SARSA handle episode termination

When applied to domains that are episodic and have a "final" state but no final action, like a game, how does SARSA update the Q-values? e.g. A game agent would receive this series: ...
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What's the difference between Adaptive Control and Hierarchical Reinforcement Learning?

After watching Travis DeWolf presentation on scaling neural computation, I'm a bit confused about the difference between Reinforcement Learning (whether hierarchical or not) and Adaptive Control. They ...
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Reinforcement learning - state space and action space

I am working on a reinforcement learning strategy for parameter control of a local search heuristic. The complete state for this RL problem can be defined as $S = \{s, p\}$, where $s$ and $p$ ...
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Hierarchical learning in a non-static environment

I've been reading about hierarchical reinforcement learning (HRL), in particular it's application to a simple delivery task as show here. While reading the paper, I noticed that the environment ...
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Why does ε-greedy $Q$-learning not oscillate?

I have a intuitive question on the convergence of $Q$- learning. In $Q$ learning for each step a $Q$- value is learned for the state-action pair where the action is selected according to the ...
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Q-learning in a Dynamic environment

I am new to reinforcement learning. Lately, I have learned Q-learning using the following tutorial. Is Q-learning still possible if the environment is dynamic. Using the environment of the tutorial ...