Evolving Reinforcement Learning Algorithms
By John D. Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Sergey Levine, Quoc V. Le, Honglak Lee, Aleksandra Faust. 2021
84 citations recorded by the source corpus.
We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. Our method can both learn from scratch and bootstrap off known existing algorithms, like DQN, enabling interpretable modifications which improve performance. Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm. Bootstrapped from DQN, we highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games. The analysis of the learned algorithm behavior shows resemblance to recently proposed RL algorithms that address overestimation in value-based methods.
Subfields: Artificial Intelligence
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Citations in the corpus
- Transformers are Meta-Reinforcement Learners (2022, background): “Another branch of methods learns the entire system using memory-based architectures (Ortega et al., 2019; Wang et al., 2017; Duan et al., 2016; Ritter et al., 2018a) or generating update rules by discovery (Oh et al., 2020) or evolution (Co-Reyes et al., 2021).”