When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
- URL: http://arxiv.org/abs/2406.07897v1
- Date: Wed, 12 Jun 2024 06:01:42 GMT
- Title: When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
- Authors: Zhening Li, Gabriel Poesia, Armando Solar-Lezama,
- Abstract summary: Skills are temporal abstractions intended to improve reinforcement learning (RL) performance.
We show theoretically and empirically that RL performance gain from skills is worse in environments where solutions to states are less compressible.
We hope our findings can guide research on automatic skill discovery and help RL practitioners better decide when and how to use skills.
- Score: 12.74839237274274
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Skills are temporal abstractions that are intended to improve reinforcement learning (RL) performance through hierarchical RL. Despite our intuition about the properties of an environment that make skills useful, a precise characterization has been absent. We provide the first such characterization, focusing on the utility of deterministic skills in deterministic sparse-reward environments with finite action spaces. We show theoretically and empirically that RL performance gain from skills is worse in environments where solutions to states are less compressible. Additional theoretical results suggest that skills benefit exploration more than they benefit learning from existing experience, and that using unexpressive skills such as macroactions may worsen RL performance. We hope our findings can guide research on automatic skill discovery and help RL practitioners better decide when and how to use skills.
Related papers
- S-EPOA: Overcoming the Indivisibility of Annotations with Skill-Driven Preference-Based Reinforcement Learning [7.8063180607224165]
Preference-based reinforcement learning (PbRL) uses human preferences as a direct reward signal.
Traditional PbRL methods are often constrained by the indivisibility of annotations, which impedes the learning process.
arXiv Detail & Related papers (2024-08-22T04:54:25Z) - Can Learned Optimization Make Reinforcement Learning Less Difficult? [70.5036361852812]
We consider whether learned optimization can help overcome reinforcement learning difficulties.
Our method, Learned Optimization for Plasticity, Exploration and Non-stationarity (OPEN), meta-learns an update rule whose input features and output structure are informed by previously proposed to these difficulties.
arXiv Detail & Related papers (2024-07-09T17:55:23Z) - EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data [22.471559284344462]
Most reinforcement learning (RL) methods focus on learning optimal policies over low-level action spaces.
While these methods can perform well in their training environments, they lack the flexibility to transfer to new tasks.
We demonstrate through experiments in sparse, image-based, robot manipulation environments that can more quickly learn new tasks than prior works.
arXiv Detail & Related papers (2024-06-25T17:50:03Z) - Constrained Ensemble Exploration for Unsupervised Skill Discovery [43.00837365639085]
Unsupervised Reinforcement Learning (RL) provides a promising paradigm for learning useful behaviors via reward-free per-training.
We propose a novel unsupervised RL framework via an ensemble of skills, where each skill performs partition exploration based on the state prototypes.
We find our method learns well-explored ensemble skills and achieves superior performance in various downstream tasks compared to previous methods.
arXiv Detail & Related papers (2024-05-25T03:07:56Z) - Choreographer: Learning and Adapting Skills in Imagination [60.09911483010824]
We present Choreographer, a model-based agent that exploits its world model to learn and adapt skills in imagination.
Our method decouples the exploration and skill learning processes, being able to discover skills in the latent state space of the model.
Choreographer is able to learn skills both from offline data, and by collecting data simultaneously with an exploration policy.
arXiv Detail & Related papers (2022-11-23T23:31:14Z) - Residual Skill Policies: Learning an Adaptable Skill-based Action Space
for Reinforcement Learning for Robotics [18.546688182454236]
Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowledge for accelerated robot learning.
We propose accelerating exploration in the skill space using state-conditioned generative models.
We validate our approach across four challenging manipulation tasks, demonstrating our ability to learn across task variations.
arXiv Detail & Related papers (2022-11-04T02:42:17Z) - Basis for Intentions: Efficient Inverse Reinforcement Learning using
Past Experience [89.30876995059168]
inverse reinforcement learning (IRL) -- inferring the reward function of an agent from observing its behavior.
This paper addresses the problem of IRL -- inferring the reward function of an agent from observing its behavior.
arXiv Detail & Related papers (2022-08-09T17:29:49Z) - Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning [27.69559938165733]
Practising and honing skills forms a fundamental component of how humans learn, yet artificial agents are rarely specifically trained to perform them.
We investigate how skills can be incorporated into the training of reinforcement learning (RL) agents in complex environments.
Our experiments show that learning with a prior knowledge of useful skills can significantly improve the performance of agents on complex problems.
arXiv Detail & Related papers (2022-07-23T19:23:29Z) - Rethinking Learning Dynamics in RL using Adversarial Networks [79.56118674435844]
We present a learning mechanism for reinforcement learning of closely related skills parameterized via a skill embedding space.
The main contribution of our work is to formulate an adversarial training regime for reinforcement learning with the help of entropy-regularized policy gradient formulation.
arXiv Detail & Related papers (2022-01-27T19:51:09Z) - RvS: What is Essential for Offline RL via Supervised Learning? [77.91045677562802]
Recent work has shown that supervised learning alone, without temporal difference (TD) learning, can be remarkably effective for offline RL.
In every environment suite we consider simply maximizing likelihood with two-layer feedforward is competitive.
They also probe the limits of existing RvS methods, which are comparatively weak on random data.
arXiv Detail & Related papers (2021-12-20T18:55:16Z) - The Information Geometry of Unsupervised Reinforcement Learning [133.20816939521941]
Unsupervised skill discovery is a class of algorithms that learn a set of policies without access to a reward function.
We show that unsupervised skill discovery algorithms do not learn skills that are optimal for every possible reward function.
arXiv Detail & Related papers (2021-10-06T13:08:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.