Primitive Enabled Adaptive Relabeling for Boosting Hierarchical Reinforcement Learning

Concetti Chiave

Primitive enabled adaptive relabeling (PEAR) generates efficient subgoal supervision by leveraging the current lower primitive's goal achieving capability, and then jointly optimizes hierarchical reinforcement learning agents using reinforcement learning and imitation learning.

Sintesi

The paper presents PEAR, a two-phase approach for hierarchical reinforcement learning (HRL) that addresses the non-stationarity issue in HRL.
In the first phase, PEAR performs adaptive relabeling on a few expert demonstrations to generate efficient subgoal supervision. It uses the current lower primitive's action value function to dynamically select achievable subgoals from the expert demonstrations, creating a curriculum of subgoals that match the lower primitive's goal-achieving capability.
In the second phase, PEAR jointly optimizes the hierarchical agents by employing both reinforcement learning (RL) and imitation learning (IL) objectives. The subgoal dataset generated in the first phase is used to provide IL-based regularization for the higher-level policy, which helps mitigate the non-stationarity issue in HRL.
The paper provides theoretical analysis to bound the sub-optimality of PEAR and derive a generalized plug-and-play framework for joint optimization using RL and IL. Extensive experiments on challenging robotic manipulation tasks demonstrate that PEAR outperforms various hierarchical and non-hierarchical baselines, showing improved performance and sample efficiency. The authors also perform real-world robotic experiments to validate the effectiveness of PEAR in complex real-world scenarios.

Statistiche

The paper reports success rate performance metrics on six MuJoCo environments: maze navigation, pick and place, bin, hollow, rope manipulation, and Franka kitchen.

Citazioni

"Hierarchical reinforcement learning (HRL) has the potential to solve complex long horizon tasks using temporal abstraction and increased exploration. However, hierarchical agents are difficult to train due to inherent non-stationarity."
"We present primitive enabled adaptive relabeling (PEAR), a two-phase approach where we first perform adaptive relabeling on a few expert demonstrations to generate efficient subgoal supervision, and then jointly optimize HRL agents by employing reinforcement learning (RL) and imitation learning (IL)."
"Since PEAR utilizes only a handful of expert demonstrations and considers minimal limiting assumptions on the task structure, it can be easily integrated with typical off-policy RL algorithms to produce a practical HRL approach."

Approfondimenti chiave tratti da

PEAR: Primitive enabled Adaptive Relabeling for boosting Hierarchical Reinforcement Learning

by Utsav Singh,... alle arxiv.org 04-23-2024

https://arxiv.org/pdf/2306.06394.pdf

Domande più approfondite

How can PEAR's adaptive relabeling approach be extended to handle undirected expert demonstrations

To extend PEAR's adaptive relabeling approach to handle undirected expert demonstrations, we can introduce a mechanism to infer the subgoals from the trajectories themselves. Instead of relying on the directionality of the expert demonstrations, we can analyze the states visited and actions taken to identify natural breakpoints or subgoals within the trajectories. By clustering similar states or actions together, we can generate subgoal supervision without the need for explicit directionality. This unsupervised approach would allow PEAR to adapt to a wider range of expert demonstration formats, making it more versatile and applicable in various scenarios.

What techniques could be explored to further reduce the overhead of periodically re-populating the subgoal dataset

To reduce the overhead of periodically re-populating the subgoal dataset in PEAR, several techniques can be explored. One approach is to implement a dynamic re-population strategy based on the learning progress of the lower primitive. Instead of re-populating the dataset at fixed intervals, the system can monitor the performance of the lower primitive and adjust the re-population frequency accordingly. If the lower primitive is making significant progress or stagnating, the re-population frequency can be dynamically adjusted to optimize learning efficiency. Additionally, techniques such as incremental relabeling, where only new expert demonstrations are used for re-population, can help reduce redundant relabeling and minimize computational costs.

Can PEAR's principles be applied to other hierarchical learning frameworks beyond reinforcement learning, such as planning or imitation learning

The principles of PEAR can indeed be applied to other hierarchical learning frameworks beyond reinforcement learning. In planning tasks, such as hierarchical task planning or task decomposition, PEAR's adaptive relabeling approach can be utilized to generate efficient subgoals or intermediate states to guide the planning process. By dynamically adjusting the subgoal supervision based on the progress of the planning algorithm, the system can adapt to changing task requirements and improve planning efficiency. Similarly, in imitation learning scenarios, PEAR's joint optimization framework can be extended to incorporate expert demonstrations in training models for complex tasks. By integrating adaptive relabeling and IL regularization, hierarchical imitation learning systems can benefit from improved sample efficiency and performance on long-horizon tasks.