Summary of Explaining An Agent’s Future Beliefs Through Temporally Decomposing Future Reward Estimators, by Mark Towers et al.
Explaining an Agent’s Future Beliefs through Temporally Decomposing Future Reward Estimators
by Mark Towers, Yali Du, Christopher Freeman, Timothy J. Norman
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 proposes a novel approach to estimating future rewards in reinforcement learning agents, specifically modifying the Q-value and state-value functions to predict the next N expected rewards. The authors refer to this method as Temporal Reward Decomposition (TRD), which unlocks new insights into an agent’s behavior. TRD enables the estimation of when an agent expects to receive a reward, its value, and the agent’s confidence in receiving it. Additionally, TRD allows for measuring the temporal importance of input features to action decisions and predicting the influence of different actions on future rewards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how computers learn from experiences. It creates a new way for computer programs called agents to predict what rewards they might get in the future. Instead of just guessing, this method breaks down the rewards into smaller pieces, so we can see when and why an agent gets a reward. This is helpful because it lets us understand how the agent makes decisions based on past experiences. |
Keywords
* Artificial intelligence * Reinforcement learning
