Reverse Forward Curriculum Learning for Extreme Sample and Demonstration Efficiency in Reinforcement Learning (2405.03379v1)

Abstract: Reinforcement learning (RL) presents a promising framework to learn policies through environment interaction, but often requires an infeasible amount of interaction data to solve complex tasks from sparse rewards. One direction includes augmenting RL with offline data demonstrating desired tasks, but past work often require a lot of high-quality demonstration data that is difficult to obtain, especially for domains such as robotics. Our approach consists of a reverse curriculum followed by a forward curriculum. Unique to our approach compared to past work is the ability to efficiently leverage more than one demonstration via a per-demonstration reverse curriculum generated via state resets. The result of our reverse curriculum is an initial policy that performs well on a narrow initial state distribution and helps overcome difficult exploration problems. A forward curriculum is then used to accelerate the training of the initial policy to perform well on the full initial state distribution of the task and improve demonstration and sample efficiency. We show how the combination of a reverse curriculum and forward curriculum in our method, RFCL, enables significant improvements in demonstration and sample efficiency compared against various state-of-the-art learning-from-demonstration baselines, even solving previously unsolvable tasks that require high precision and control.

• The paper introduces a two-stage curriculum combining reverse and forward strategies to overcome sparse rewards in RL.
• It leverages per-demonstration reverse curricula to enable learning with as few as five demonstrations for high-precision tasks.
• The method significantly outperforms baselines on complex robotics tasks, offering a scalable solution for sample-efficient RL.

An Analysis of "Reverse Forward Curriculum Learning for Extreme Sample and Demo Efficiency"

The paper presents a novel approach termed Reverse Forward Curriculum Learning (RFCL), a framework designed to enhance sample and demonstration efficiency in reinforcement learning (RL), particularly for complex, high-dimensional tasks with sparse rewards. This method addresses the sample inefficiency typical of RL when trained from scratch, especially in environments with difficult exploration problems—common in robotics domains—and limited high-quality demonstration data.

Methodological Innovations

RFCL introduces a two-stage curriculum learning strategy: reverse curriculum followed by a forward curriculum. The reverse forward curriculum is distinct due to its capacity to leverage multiple demonstrations through a per-demonstration reverse curriculum strategy, contrasted with previous works often requiring abundant, high-quality demonstration data. This approach uniquely combines two complementary curriculums:

1. Reverse Curriculum: This stage is initiated by resetting states in accordance with pre-collected demonstration data. Unlike traditional approaches that use a static reset mechanism, RFCL constructs a per-demonstration curriculum, iterating backward from success states. This provides a method to overcome significant exploration challenges by initially focusing on a narrow distribution of initial states derived from demonstrations. The reverse curriculum helps establish an initial policy capable of performing tasks from these states efficiently.
2. Forward Curriculum: Once an initial policy is developed, it undergoes further refinement through a forward curriculum. This involves broadening the policy’s utility across a fuller initial state distribution. The forward curriculum employs a prioritized sampling mechanism, wherein training focuses on states that edge the policy's capabilities. This staged advancement eases the transition from solving tasks under controlled conditions (demonstrated states) to solving them in more general, varied environments.

The reverse and forward curriculums together facilitate more efficient learning by systematically guiding the policy from mastery over a restricted state space to proficiency across the complete state distribution.

Empirical Evaluation and Results

The empirical validation of RFCL demonstrates significant advancements over existing learning-from-demonstration methods. The authors benchmarked RFCL against state-of-the-art baselines, such as RLPD and JSRL, across 21 robotics tasks in the Adroit, ManiSkill2, and MetaWorld environments.

Quantitative Highlights:

• RFCL outperformed other methods, particularly on complex, high-precision tasks like dexterous manipulation, where traditional methods failed.
• The method achieved high performance with as few as five demonstration samples, an impressive reduction compared to the larger datasets typically necessary for competitive baselines.

The results underline RFCL’s enhanced sample efficiency, showcasing its ability to solve tasks within constrained computational budgets significantly faster than its predecessors, and even tackling tasks previously marked unsolvable with sparse rewards.

Implications and Future Directions

The theoretic and practical implications of RFCL are notable. By demonstrating the viability of a reverse forward curriculum, the paper contributes to curriculum learning literature, illustrating a scalable path to efficient learning in sparse reward environments. This approach holds promise for tasks where collecting extensive demonstration datasets is impractical.

For future exploration, the RFCL method could be combined with model-based methods or applied to real-world scenarios through sim-to-real methodologies, potentially broadening its application scope. Additionally, further research might delve into automatic demonstration collection strategies, enhancing RFCL’s utility in dynamic environments.

In sum, Reverse Forward Curriculum Learning is a substantial progression towards efficient reinforcement learning by exploiting the synergy between reverse and forward curriculums. Its introduction furthers the potential of RL systems to learn complex tasks effectively, setting the stage for continued innovation in AI and robotics.