Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.10792 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909495815307264 |
|---|---|
| author | Ollivier, Yann |
| author_facet | Ollivier, Yann |
| contents | Zero-shot reinforcement learning (RL) methods aim at instantly producing a behavior for an RL task in a given environment, from a description of the reward function. These methods are usually tested by evaluating their average performance on a series of downstream tasks. Yet they cannot be trained directly for that objective, unless the distribution of downstream tasks is known. Existing approaches either use other learning criteria [BBQ+ 18, TRO23, TO21, HDB+ 19], or explicitly set a prior on downstream tasks, such as reward functions given by a random neural network [FPAL24].
Here we prove that the zero-shot RL loss can be optimized directly, for a range of non-informative priors such as white noise rewards, temporally smooth rewards, ``scattered'' sparse rewards, or a combination of those.
Thus, it is possible to learn the optimal zero-shot features algorithmically, for a wide mixture of priors.
Surprisingly, the white noise prior leads to an objective almost identical to the one in VISR [HDB+19], via a different approach. This shows that some seemingly arbitrary choices in VISR, such as Von Mises--Fisher distributions, do maximize downstream performance. This also suggests more efficient ways to tackle the VISR objective.
Finally, we discuss some consequences and limitations of the zero-shot RL objective, such as its tendency to produce narrow optimal features if only using Gaussian dense reward priors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10792 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tackling the Zero-Shot Reinforcement Learning Loss Directly Ollivier, Yann Machine Learning Zero-shot reinforcement learning (RL) methods aim at instantly producing a behavior for an RL task in a given environment, from a description of the reward function. These methods are usually tested by evaluating their average performance on a series of downstream tasks. Yet they cannot be trained directly for that objective, unless the distribution of downstream tasks is known. Existing approaches either use other learning criteria [BBQ+ 18, TRO23, TO21, HDB+ 19], or explicitly set a prior on downstream tasks, such as reward functions given by a random neural network [FPAL24]. Here we prove that the zero-shot RL loss can be optimized directly, for a range of non-informative priors such as white noise rewards, temporally smooth rewards, ``scattered'' sparse rewards, or a combination of those. Thus, it is possible to learn the optimal zero-shot features algorithmically, for a wide mixture of priors. Surprisingly, the white noise prior leads to an objective almost identical to the one in VISR [HDB+19], via a different approach. This shows that some seemingly arbitrary choices in VISR, such as Von Mises--Fisher distributions, do maximize downstream performance. This also suggests more efficient ways to tackle the VISR objective. Finally, we discuss some consequences and limitations of the zero-shot RL objective, such as its tendency to produce narrow optimal features if only using Gaussian dense reward priors. |
| title | Tackling the Zero-Shot Reinforcement Learning Loss Directly |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.10792 |