Dec 21, 2022 � We study neighboring state-based, model-free exploration led by the intuition that, for an early-stage agent, considering actions derived from a bounded region�...
In this work, we study neighboring state-based, model-free exploration led by the intuition that, for an early-stage agent, considering actions derived from a�...
In this work, we study neighboring state-based, model-free exploration led by the intuition that, for an early-stage agent, considering actions derived from a�...
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Reinforcement Learning is a powerful tool to model decision-making processes. However, it relies on an exploration-exploitation trade-off that remains an�...
May 2, 2023 � I am looking for metrics to compare the exploration under different RL Algos/reward functions. I want to somehow quantify how big of a region of the policy�...
In this paper, we present a novel exploration technique that maximizes the value-conditional state entropy, which separately estimates the state entropies that�...
Neighboring States-based RL Exploration. Repo for this ArXiv paper. All training scripts are located in the scripts/ directory. To be updated with more�...
This paper employs state similarity to improve rein- forcement learning performance. This is achieved by first identifying states with similar sub-policies.
In online reinforcement learning (RL), efficient exploration remains particularly challenging in high-dimensional environments with sparse rewards. In low-.
In this paper, we consider the important problem of safe exploration in reinforcement learning. While reinforcement learning is well-suited to domains with�...
