Summary of A Role Of Environmental Complexity on Representation Learning in Deep Reinforcement Learning Agents, by Andrew Liu et al.
A Role of Environmental Complexity on Representation Learning in Deep Reinforcement Learning Agents
by Andrew Liu, Alla Borisyuk
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a simulated environment for training deep reinforcement learning agents on a navigation task inspired by human navigators’ dual solution paradigm. The researchers manipulated the frequency of shortcut exposure and cue presentation to investigate how these factors influence shortcut usage development. Results show that all agents quickly achieve optimal performance in closed trials, but those with higher shortcut exposure navigate faster and use shortcuts more frequently in open trials. Analysis of artificial neural networks reveals that frequent cue presentation leads to better encoding, but stronger cues are formed through navigation planning rather than exposure alone. The study also finds that spatial representations develop early in training and stabilize before navigation strategies are fully developed. Furthermore, the planned trajectory is encoded at the population level, not individual node level. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a simulated environment to train computer programs called “agents” to navigate through a maze. The agents can take shortcuts or follow longer paths to reach their goal. Researchers wanted to see how often the agents use these shortcuts and what factors influence this decision-making process. They found that all agents quickly learn the best route, but some agents that are exposed to the shortcut more frequently use it more often. Analysis of the agent’s “brain” shows that when they’re first learning, exposure to the cue helps them understand it better. But as they get better at navigating, the cue becomes less important and their overall navigation strategy improves. This study could have broader implications for understanding how humans navigate through complex environments. |
Keywords
* Artificial intelligence * Reinforcement learning
