Adaptive Skill Distribution Enhances Goal Exploration Efficiency in Reinforcement Learning

מושגי ליבה

Leveraging an adaptive skill distribution based on local entropy optimization can significantly improve exploration efficiency in goal-conditioned reinforcement learning tasks with sparse rewards and long horizons.

תקציר

The paper introduces a novel framework called Goal Exploration via Adaptive Skill Distribution (GEASD) to address the challenge of efficient exploration in goal-conditioned reinforcement learning (GCRL) tasks with sparse rewards and long horizons.

Key highlights:

• GEASD utilizes an adaptive skill distribution that optimizes the local entropy of achieved goals within a historical context, enabling the agent to leverage environmental structural patterns and facilitate deep exploration.
• The skill distribution is modeled as a Boltzmann distribution derived from skill value functions, which capture the expected local entropy changes for each skill.
• Novel intrinsic rewards are introduced to learn the skill value functions, reflecting the local entropy variations during the evolution of the historical context.
• Theoretical analysis justifies the use of the Boltzmann distribution for the skill distribution and the relationship between the local entropy changes and the overall exploration objective.
• Experiments on two challenging GCRL tasks demonstrate that GEASD significantly outperforms state-of-the-art exploration methods in terms of exploration efficiency, success rate, and entropy of achieved goals.
• The learned skill distribution also exhibits robust generalization capabilities, enabling efficient exploration in unseen tasks with similar local structures.

סטטיסטיקה

The agent's success rate in reaching the desired goal is a key metric used to evaluate the exploration efficiency.
The entropy of achieved goals is another important metric that estimates the coverage of the explored space.

ציטוטים

"Exploration efficiency poses a significant challenge in goal-conditioned reinforcement learning (GCRL) tasks, particularly those with long horizons and sparse rewards."
"We introduce a novel framework, GEASD, designed to capture these patterns through an adaptive skill distribution during the learning process."
"Our experiments reveal marked improvements in exploration efficiency using the adaptive skill distribution compared to a uniform skill distribution."

תובנות מפתח מזוקקות מ:

Goal Exploration via Adaptive Skill Distribution for Goal-Conditioned Reinforcement Learning

by Lisheng Wu,K... ב- arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12999.pdf

שאלות מעמיקות

How can the GEASD framework be extended to handle continuous skill spaces instead of the discrete skill set used in this work

To extend the GEASD framework to handle continuous skill spaces, we can utilize techniques such as policy gradient methods or actor-critic architectures. By parameterizing the skill distribution as a continuous function, we can employ algorithms like Proximal Policy Optimization (PPO) or Soft Actor-Critic (SAC) to learn the optimal skill distribution. This approach allows for a more fine-grained control over the skill selection process, enabling the agent to smoothly transition between different skills in a continuous space. Additionally, we can incorporate techniques like entropy regularization to encourage exploration and prevent the distribution from collapsing to a single skill.

What are the potential limitations of the Boltzmann distribution assumption, and how could the skill distribution be further improved

The Boltzmann distribution assumption in the skill distribution may have limitations in scenarios where the skill values are not well-separated or when the skill space is high-dimensional. In such cases, the Boltzmann distribution may struggle to effectively capture the relative importance of different skills. To address this limitation, we could explore alternative distributional forms that can better model the skill values, such as using a Gaussian distribution or a mixture of distributions. Additionally, incorporating a temperature parameter that adapts dynamically based on the local entropy could further enhance the exploration capabilities of the agent. By continuously adjusting the temperature, the agent can strike a balance between exploitation and exploration, leading to more efficient learning and goal achievement.

Can the insights from the GEASD framework be applied to other exploration-focused reinforcement learning problems beyond goal-conditioned tasks

The insights from the GEASD framework can be applied to a wide range of exploration-focused reinforcement learning problems beyond goal-conditioned tasks. For example, in robotic manipulation tasks, where the agent needs to learn complex manipulation skills, the adaptive skill distribution mechanism can help the agent explore different manipulation strategies efficiently. In autonomous driving scenarios, the agent can benefit from adaptive skill selection to navigate complex road environments and handle diverse driving conditions. Furthermore, in multi-agent systems, the GEASD framework can be used to enable agents to learn diverse strategies and adapt to changing environments collaboratively. Overall, the principles of adaptive skill distribution and deep exploration can be generalized to various reinforcement learning domains to improve learning efficiency and goal achievement.