Long-term visitation value for deep exploration in sparse-reward reinforcement learning
Parisi, Simone ; Tateo, Davide LU
; Hensel, Maximilian
; D’eramo, Carlo
; Peters, Jan
and Pajarinen, Joni
(2022)
In Algorithms
15(3).
- Abstract
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Similarly, if the agent receives also rewards that create suboptimal modes of the objective function, it will likely prematurely stop exploring. More recent methods add auxiliary intrinsic rewards to encourage exploration. However, auxiliary rewards lead to a non-stationary target for the Q-function. In this paper, we present a novel approach that (1) plans exploration actions far into the future by using a long-term visitation count, and (2) decouples exploration and exploitation by... (More)
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Similarly, if the agent receives also rewards that create suboptimal modes of the objective function, it will likely prematurely stop exploring. More recent methods add auxiliary intrinsic rewards to encourage exploration. However, auxiliary rewards lead to a non-stationary target for the Q-function. In this paper, we present a novel approach that (1) plans exploration actions far into the future by using a long-term visitation count, and (2) decouples exploration and exploitation by learning a separate function assessing the exploration value of the actions. Contrary to existing methods that use models of reward and dynamics, our approach is off-policy and model-free. We further propose new tabular environments for benchmarking exploration in reinforcement learning. Empirical results on classic and novel benchmarks show that the proposed approach outperforms existing methods in environments with sparse rewards, especially in the presence of rewards that create suboptimal modes of the objective function. Results also suggest that our approach scales gracefully with the size of the environment.
(Less)
- author
- Parisi, Simone
; Tateo, Davide
LU
; Hensel, Maximilian
; D’eramo, Carlo
; Peters, Jan
and Pajarinen, Joni
- publishing date
- 2022-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Exploration, Off-policy, Reinforcement learning, Sparse reward, Upper confidence bound
- in
- Algorithms
- volume
- 15
- issue
- 3
- article number
- 81
- publisher
- MDPI AG
- external identifiers
-
- scopus:85126668173
- ISSN
- 1999-4893
- DOI
- 10.3390/a15030081
- language
- English
- LU publication?
- no
- id
- 99995be4-fac2-4ba9-98ac-24dadb4ad558
- date added to LUP
- 2025-10-16 14:34:28
- date last changed
- 2025-10-22 03:43:15
@article{99995be4-fac2-4ba9-98ac-24dadb4ad558,
abstract = {{<p>Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Similarly, if the agent receives also rewards that create suboptimal modes of the objective function, it will likely prematurely stop exploring. More recent methods add auxiliary intrinsic rewards to encourage exploration. However, auxiliary rewards lead to a non-stationary target for the Q-function. In this paper, we present a novel approach that (1) plans exploration actions far into the future by using a long-term visitation count, and (2) decouples exploration and exploitation by learning a separate function assessing the exploration value of the actions. Contrary to existing methods that use models of reward and dynamics, our approach is off-policy and model-free. We further propose new tabular environments for benchmarking exploration in reinforcement learning. Empirical results on classic and novel benchmarks show that the proposed approach outperforms existing methods in environments with sparse rewards, especially in the presence of rewards that create suboptimal modes of the objective function. Results also suggest that our approach scales gracefully with the size of the environment.</p>}},
author = {{Parisi, Simone and Tateo, Davide and Hensel, Maximilian and D’eramo, Carlo and Peters, Jan and Pajarinen, Joni}},
issn = {{1999-4893}},
keywords = {{Exploration; Off-policy; Reinforcement learning; Sparse reward; Upper confidence bound}},
language = {{eng}},
number = {{3}},
publisher = {{MDPI AG}},
series = {{Algorithms}},
title = {{Long-term visitation value for deep exploration in sparse-reward reinforcement learning}},
url = {{http://dx.doi.org/10.3390/a15030081}},
doi = {{10.3390/a15030081}},
volume = {{15}},
year = {{2022}},
}