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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2512.06471 |
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| _version_ | 1866913128050065408 |
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| author | Lawrence, Nathan P. Mesbah, Ali |
| author_facet | Lawrence, Nathan P. Mesbah, Ali |
| contents | Goal-conditioned reinforcement learning (RL) concerns the problem of training an agent to maximize the probability of reaching target goal states. This paper presents an analysis of the goal-conditioned setting based on optimal control. In particular, we derive an optimality gap between more classical, often quadratic, objectives and the goal-conditioned reward, elucidating the success of goal-conditioned RL and why classical ``dense'' rewards can falter. We then consider the partially observed Markov decision setting and connect state estimation to our probabilistic reward, making the goal-conditioned reward well suited to dual control problems. The advantages of goal-conditioned policies are validated on nonlinear and uncertain environments using both RL and predictive control techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06471 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Why Goal-Conditioned Reinforcement Learning Works: Relation to Dual Control Lawrence, Nathan P. Mesbah, Ali Machine Learning Artificial Intelligence Goal-conditioned reinforcement learning (RL) concerns the problem of training an agent to maximize the probability of reaching target goal states. This paper presents an analysis of the goal-conditioned setting based on optimal control. In particular, we derive an optimality gap between more classical, often quadratic, objectives and the goal-conditioned reward, elucidating the success of goal-conditioned RL and why classical ``dense'' rewards can falter. We then consider the partially observed Markov decision setting and connect state estimation to our probabilistic reward, making the goal-conditioned reward well suited to dual control problems. The advantages of goal-conditioned policies are validated on nonlinear and uncertain environments using both RL and predictive control techniques. |
| title | Why Goal-Conditioned Reinforcement Learning Works: Relation to Dual Control |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.06471 |