How do different learning strategies affect the exploration process?
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Different learning strategies can have an impact on the exploration process in reinforcement learning. One approach is to use a mixture of experts (MoE) policy representation, which consists of multiple stochastic sub-policies that explore distinct regions of the action space. This allows for efficient adaptation in new environments and promotes the discovery of good strategies . Another factor to consider is the agent's exploration strategy, which can affect how quickly it learns to solve different problems. Methods that add a bonus to the value function tend to explore more effectively than those that add a bonus to rewards . Linear Upper Confidence Least Squares is one method that consistently performs well in various exploration environments . Overall, the choice of learning strategy can significantly impact the exploration process in reinforcement learning.
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01 Jan 2020 | The paper does not directly address how different learning strategies affect the exploration process. The paper focuses on evaluating the performance of several exploration methods on prototypical exploration environments. |
| The paper does not explicitly discuss different learning strategies and their effects on the exploration process. | |
08 Mar 2023 | The provided paper does not explicitly discuss different learning strategies and their effects on the exploration process. |
3 Citations | The provided paper does not explicitly discuss different learning strategies and their effects on the exploration process. |
| The paper does not explicitly discuss different learning strategies and their effects on the exploration process. |
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