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Found 3 papers, 2 papers with code
Learning Top-k Subtask Planning Tree based on Discriminative Representation Pre-training for Decision Making
Many complicated real-world tasks can be broken down into smaller, more manageable parts, and planning with prior knowledge extracted from these simplified pieces is crucial for humans to make accurate decisions.
Balancing Exploration and Exploitation in Hierarchical Reinforcement Learning via Latent Landmark Graphs
However, existing works often overlook the temporal coherence in GCHRL when learning latent subgoal representations and lack an efficient subgoal selection strategy that balances exploration and exploitation.
Explainable Reinforcement Learning via a Causal World Model
Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future.
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