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This paper introduces G2QDR, a framework that leverages directed state graphs to generate dense rewards for goal-conditioned hierarchical reinforcement learning, enhancing performance in sparse reward environments.
This paper proposes a two-level hierarchical reinforcement learning framework called ToSCA for conversational agents, which uses DQN and PPO with a dual-granularity reward mechanism to improve strategy determination and response quality in multi-turn conversations.
This paper proposes HT-GRPO, a hierarchical reinforcement learning method for diffusion multi-modal large language models that uses a sketch-then-paint training scheme and hierarchical credit assignment to improve image generation quality and reward alignment.
OpenAI research proposes hierarchical reinforcement learning where agents break down complex tasks into sequences of high-level actions rather than low-level ones, significantly improving efficiency for long-horizon tasks by reducing search complexity from thousands of steps to dozens.