Action-Curiosity-Based Deep Reinforcement Learning Algorithm for Path Planning in a Nondeterministic Environment
In the field of path planning, the efficiency and effectiveness of deep reinforcement learning (DRL) methods are often constrained by the algorithms’ exploration capabilities, particularly in dynamic and nondeterministic environments. This paper introduces a novel DRL optimization approach predicate...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Intelligent Computing |
| Online Access: | https://spj.science.org/doi/10.34133/icomputing.0140 |
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