Optimal Use of Experience in First Person Shooter Environments Article Swipe
Although reinforcement learning has made great strides recently, a continuing limitation is that it requires an extremely high number of interactions with the environment. In this paper, we explore the effectiveness of reusing experience from the experience replay buffer in the Deep Q-Learning algorithm. We test the effectiveness of applying learning update steps multiple times per environmental step in the VizDoom environment and show first, this requires a change in the learning rate, and second that it does not improve the performance of the agent. Furthermore, we show that updating less frequently is effective up to a ratio of 4:1, after which performance degrades significantly. These results quantitatively confirm the widespread practice of performing learning updates every 4th environmental step.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/cig.2019.8848049
- OA Status
- green
- References
- 24
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2952204091
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2952204091Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/cig.2019.8848049Digital Object Identifier
- Title
-
Optimal Use of Experience in First Person Shooter EnvironmentsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-08-01Full publication date if available
- Authors
-
Matthew AitchisonList of authors in order
- Landing page
-
https://doi.org/10.1109/cig.2019.8848049Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1906.09734Direct OA link when available
- Concepts
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Reuse, Computer science, Reinforcement learning, Artificial intelligence, Machine learning, Test (biology), Learning environment, Human–computer interaction, Engineering, Psychology, Mathematics education, Paleontology, Biology, Waste managementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
24Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.algorithm. | 43 |
| abstract_inverted_index.continuing | 9 |
| abstract_inverted_index.experience | 33, 36 |
| abstract_inverted_index.frequently | 91 |
| abstract_inverted_index.limitation | 10 |
| abstract_inverted_index.performing | 113 |
| abstract_inverted_index.widespread | 110 |
| abstract_inverted_index.environment | 61 |
| abstract_inverted_index.performance | 81, 102 |
| abstract_inverted_index.Furthermore, | 85 |
| abstract_inverted_index.environment. | 23 |
| abstract_inverted_index.interactions | 20 |
| abstract_inverted_index.effectiveness | 30, 47 |
| abstract_inverted_index.environmental | 56, 118 |
| abstract_inverted_index.reinforcement | 1 |
| abstract_inverted_index.quantitatively | 107 |
| abstract_inverted_index.significantly. | 104 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5005322233 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I118347636 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.05947955 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |