Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration (2202.04628v2)

Abstract: A major challenge in real-world reinforcement learning (RL) is the sparsity of reward feedback. Often, what is available is an intuitive but sparse reward function that only indicates whether the task is completed partially or fully. However, the lack of carefully designed, fine grain feedback implies that most existing RL algorithms fail to learn an acceptable policy in a reasonable time frame. This is because of the large number of exploration actions that the policy has to perform before it gets any useful feedback that it can learn from. In this work, we address this challenging problem by developing an algorithm that exploits the offline demonstration data generated by a sub-optimal behavior policy for faster and efficient online RL in such sparse reward settings. The proposed algorithm, which we call the Learning Online with Guidance Offline (LOGO) algorithm, merges a policy improvement step with an additional policy guidance step by using the offline demonstration data. The key idea is that by obtaining guidance from - not imitating - the offline data, LOGO orients its policy in the manner of the sub-optimal policy, while yet being able to learn beyond and approach optimality. We provide a theoretical analysis of our algorithm, and provide a lower bound on the performance improvement in each learning episode. We also extend our algorithm to the even more challenging incomplete observation setting, where the demonstration data contains only a censored version of the true state observation. We demonstrate the superior performance of our algorithm over state-of-the-art approaches on a number of benchmark environments with sparse rewards and censored state. Further, we demonstrate the value of our approach via implementing LOGO on a mobile robot for trajectory tracking and obstacle avoidance, where it shows excellent performance.

Reinforcement Learning with Sparse Rewards Using Guidance from Offline Demonstration

The paper addresses the challenge of sparse rewards in real-world reinforcement learning (RL), proposing an innovative algorithm termed Learning Online with Guidance Offline (LOGO). In sparse reward settings, the available feedback is often limited to whether a task is partially or fully completed, making the process of learning an efficient policy through exploration alone prohibitively time-consuming. This work utilizes offline demonstration data from a sub-optimal behavior policy to enhance online learning efficiency.

The LOGO algorithm distinguishes itself by combining a policy improvement step, grounded in trust region policy optimization (TRPO), with a novel policy guidance step informed by offline demonstrations. This two-step approach allows the algorithm to utilize the demonstration data not for imitation but for exploration guidance. The aim is to initially align the policy with the sub-optimal behavior policy, leveraging its advantages while progressively guiding the learning process toward optimality.

Theoretical analysis establishes a performance improvement guarantee per episode, quantifying how the guidance from demonstrations can initially accelerate policy learning in environments with sparse rewards. A notable contribution is a performance difference lemma tailored for policy-dependent reward functions, which underpins the surrogate objective function used in the policy guidance step. This surrogate objective enables implementations leveraging existing TRPO frameworks, as LOGO is executed through TRPO-like steps. Empirically, the algorithm demonstrates superior performance against state-of-the-art methods across several benchmark sparse reward environments.

Moreover, the paper extends LOGO to handle incomplete observation settings, where demonstrations are only available in a reduced state space. This feature broadens the practical applicability of the algorithm, which is validated by experiments on both benchmark environments and a real-world robotic setup involving a mobile robot for trajectory tracking and obstacle avoidance.

In terms of implications, the advances presented in this paper suggest potential in several areas: improving RL efficiency in resource-constrained settings, better handling of exploration in environments with sparse signals, and more general application to real-world problems where reward design is complex or costly. The concept of using sub-optimal behavior policies as initial guidance introduces a paradigm shift in leveraging existing data, potentially inspiring future work in offline RL and imitation learning. The demonstration of LOGO on robotic platforms also suggests avenues for applying RL to complex robotics problems where state and reward spaces are incomplete or overly simplistic due to practical constraints.

The methodology and results of this paper signify an incremental yet important step forward in the field of reinforcement learning. The improved learning efficiency and broader applicability could stimulate further research into hybrid approaches that combine offline and online learning strategies. Future work may explore expanding on these concepts by incorporating advanced demonstration data forms and learning policies in multi-agent RL settings or domains with hierarchical structures.