A decoupled hierarchical RL framework with a rule-based low-level policy and DeepMDP state abstraction outperforms PPO on two custom discrete grid environments, but with a single baseline and sparse experimental detail.
Why generalization in RL is difficult: Epistemic POMDPs and implicit partial observability,
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Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids
A decoupled hierarchical RL framework with a rule-based low-level policy and DeepMDP state abstraction outperforms PPO on two custom discrete grid environments, but with a single baseline and sparse experimental detail.