Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908865677754368 |
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| author | Cherepanov, Egor Kachaev, Nikita Zholus, Artem Kovalev, Alexey K. Panov, Aleksandr I. |
| author_facet | Cherepanov, Egor Kachaev, Nikita Zholus, Artem Kovalev, Alexey K. Panov, Aleksandr I. |
| contents | The incorporation of memory into agents is essential for numerous tasks within the domain of Reinforcement Learning (RL). In particular, memory is paramount for tasks that require the use of past information, adaptation to novel environments, and improved sample efficiency. However, the term "memory" encompasses a wide range of concepts, which, coupled with the lack of a unified methodology for validating an agent's memory, leads to erroneous judgments about agents' memory capabilities and prevents objective comparison with other memory-enhanced agents. This paper aims to streamline the concept of memory in RL by providing practical precise definitions of agent memory types, such as long-term vs. short-term memory and declarative vs. procedural memory, inspired by cognitive science. Using these definitions, we categorize different classes of agent memory, propose a robust experimental methodology for evaluating the memory capabilities of RL agents, and standardize evaluations. Furthermore, we empirically demonstrate the importance of adhering to the proposed methodology when evaluating different types of agent memory by conducting experiments with different RL agents and what its violation leads to. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06531 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation Cherepanov, Egor Kachaev, Nikita Zholus, Artem Kovalev, Alexey K. Panov, Aleksandr I. Machine Learning Artificial Intelligence The incorporation of memory into agents is essential for numerous tasks within the domain of Reinforcement Learning (RL). In particular, memory is paramount for tasks that require the use of past information, adaptation to novel environments, and improved sample efficiency. However, the term "memory" encompasses a wide range of concepts, which, coupled with the lack of a unified methodology for validating an agent's memory, leads to erroneous judgments about agents' memory capabilities and prevents objective comparison with other memory-enhanced agents. This paper aims to streamline the concept of memory in RL by providing practical precise definitions of agent memory types, such as long-term vs. short-term memory and declarative vs. procedural memory, inspired by cognitive science. Using these definitions, we categorize different classes of agent memory, propose a robust experimental methodology for evaluating the memory capabilities of RL agents, and standardize evaluations. Furthermore, we empirically demonstrate the importance of adhering to the proposed methodology when evaluating different types of agent memory by conducting experiments with different RL agents and what its violation leads to. |
| title | Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.06531 |