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Found 3 papers, 1 papers with code
RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$
Meta reinforcement learning (meta-RL) methods such as RL$^2$ have emerged as promising approaches for learning data-efficient RL algorithms tailored to a given task distribution.
Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL
Model-based reinforcement learning promises to learn an optimal policy from fewer interactions with the environment compared to model-free reinforcement learning by learning an intermediate model of the environment in order to predict future interactions.
Resource Constrained Deep Reinforcement Learning
However, existing Deep RL methods are unable to handle combinatorial action spaces and constraints on allocation of resources.
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