Researchers have developed a novel two-level hierarchical reinforcement learning (RL) framework called ToSCA for conversational agents. This approach bridges the gap between existing token-level or utterance-level RL methods by incorporating both temporal and strategic abstractions. The framework utilizes Deep Q-Network (DQN) for the high-level critic and Proximal Policy Optimization (PPO) for the low-level actor-critic, addressing reward sparsity with a dual-granularity reward mechanism. Experiments demonstrate that ToSCA improves strategy determination and response quality in both daily and emotional support conversations compared to existing baselines. AI
IMPACT This new framework could lead to more sophisticated and context-aware conversational AI systems.
RANK_REASON The cluster contains a research paper detailing a new framework for conversational agents. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Deep Q-Network
- Hierarchical reinforcement learning and decision making
- Markov decision process
- Proximal Policy Optimization
- reinforcement learning
- ToSCA
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