Guided Cooperation in Hierarchical Reinforcement Learning via Model-based Rollout

• URL: http://arxiv.org/abs/2309.13508v2
• Date: Sat, 6 Apr 2024 17:07:13 GMT
• Title: Guided Cooperation in Hierarchical Reinforcement Learning via Model-based Rollout
• Authors: Haoran Wang, Zeshen Tang, Leya Yang, Yaoru Sun, Fang Wang, Siyu Zhang, Yeming Chen,
• Abstract summary: We propose a goal-conditioned hierarchical reinforcement learning (HRL) framework named Guided Cooperation via Model-based Rollout (GCMR) GCMR aims to bridge inter-layer information synchronization and cooperation by exploiting forward dynamics. Experimental results demonstrate that incorporating the proposed GCMR framework with a disentangled variant of HIGL, namely ACLG, yields more stable and robust policy improvement.
• Score: 16.454305212398328
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Goal-conditioned hierarchical reinforcement learning (HRL) presents a promising approach for enabling effective exploration in complex, long-horizon reinforcement learning (RL) tasks through temporal abstraction. Empirically, heightened inter-level communication and coordination can induce more stable and robust policy improvement in hierarchical systems. Yet, most existing goal-conditioned HRL algorithms have primarily focused on the subgoal discovery, neglecting inter-level cooperation. Here, we propose a goal-conditioned HRL framework named Guided Cooperation via Model-based Rollout (GCMR), aiming to bridge inter-layer information synchronization and cooperation by exploiting forward dynamics. Firstly, the GCMR mitigates the state-transition error within off-policy correction via model-based rollout, thereby enhancing sample efficiency. Secondly, to prevent disruption by the unseen subgoals and states, lower-level Q-function gradients are constrained using a gradient penalty with a model-inferred upper bound, leading to a more stable behavioral policy conducive to effective exploration. Thirdly, we propose a one-step rollout-based planning, using higher-level critics to guide the lower-level policy. Specifically, we estimate the value of future states of the lower-level policy using the higher-level critic function, thereby transmitting global task information downwards to avoid local pitfalls. These three critical components in GCMR are expected to facilitate inter-level cooperation significantly. Experimental results demonstrate that incorporating the proposed GCMR framework with a disentangled variant of HIGL, namely ACLG, yields more stable and robust policy improvement compared to various baselines and significantly outperforms previous state-of-the-art algorithms.

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