HiER: Highlight Experience Replay for Boosting Off-Policy Reinforcement Learning Agents (2312.09394v3)

Abstract: Even though reinforcement-learning-based algorithms achieved superhuman performance in many domains, the field of robotics poses significant challenges as the state and action spaces are continuous, and the reward function is predominantly sparse. Furthermore, on many occasions, the agent is devoid of access to any form of demonstration. Inspired by human learning, in this work, we propose a method named highlight experience replay (HiER) that creates a secondary highlight replay buffer for the most relevant experiences. For the weights update, the transitions are sampled from both the standard and the highlight experience replay buffer. It can be applied with or without the techniques of hindsight experience replay (HER) and prioritized experience replay (PER). Our method significantly improves the performance of the state-of-the-art, validated on 8 tasks of three robotic benchmarks. Furthermore, to exploit the full potential of HiER, we propose HiER+ in which HiER is enhanced with an arbitrary data collection curriculum learning method. Our implementation, the qualitative results, and a video presentation are available on the project site: http://www.danielhorvath.eu/hier/.

• The paper introduces HiER, a dual-buffer replay strategy that prioritizes high-reward transitions to significantly enhance off-policy RL performance.
• It proposes Easy2Hard Initial State Entropy (E2H-ISE), a curriculum approach that gradually increases initial state complexity to improve exploration.
• Integrating HiER and E2H-ISE into HiER+ yields superior success rates in robotic manipulation tasks compared to standard replay methods.

HiER: Enhancing Off-Policy Reinforcement Learning with Adaptive Experience Replay and Curriculum Learning

The paper "HiER: Highlight Experience Replay and Easy2Hard Curriculum Learning for Boosting Off-Policy Reinforcement Learning Agents" introduces two innovative techniques aimed at improving the efficiency and performance of off-policy reinforcement learning (RL) agents, particularly in robotic environments characterized by continuous state-action spaces and sparse reward functions. The authors, Dániel Horváth and his colleagues, provide a detailed exploration of these techniques — Highlight Experience Replay (HiER) and Easy2Hard Initial State Entropy (E2H-ISE) — along with their integration into a combined method termed HiER+.

Core Contributions

1. Highlight Experience Replay (HiER): This method establishes a secondary replay buffer, wherein crucial transitions — typically those associated with higher rewards — are stored and replayed more frequently. HiER operates by setting a threshold based on cumulative rewards; episodes surpassing this threshold have their transitions added to the highlight buffer as well as the standard replay buffer. This dual-buffer strategy addresses the sparseness of reward signals and enhances learning efficiency by focusing on transitions that are more informative or indicative of successful behavior.
2. Easy2Hard Initial State Entropy (E2H-ISE): E2H-ISE is a curriculum learning strategy that modulates the initial state distribution's entropy over the training period. Initially, the RL agent is exposed to simpler tasks by limiting the diversity of initial states (low entropy), subsequently increasing difficulty by broadening this distribution towards uniformity (higher entropy). This progressive challenge increase mirrors how learning occurs naturally in human and animal learning processes, where simpler tasks are mastered before advancing to more complex ones.
3. HiER+: The synergistic integration of HiER and E2H-ISE, termed HiER+, offers substantial improvements by combining advanced transition sampling with curriculum-based state initialization. Experimental results demonstrate that this hybrid approach significantly outperforms baseline models as well as state-of-the-art methods, particularly in complex robotic manipulation tasks such as push, slide, and pick-and-place.

Experimental Insights

The experimental validation of HiER+ on the panda-gym robotic benchmark substantiates its efficacy. The techniques showed consistent improvements across different environments, achieving mean success rates of up to 1.0 (push), 0.83 (slide), and 0.69 (pick-and-place) when paired with Soft Actor-Critic (SAC). These results indicate a robust enhancement over traditional HER and PER (Prioritized Experience Replay) techniques. Notably, HiER alone provides noticeable improvement, demonstrating its standalone potential, while E2H-ISE modifies the task difficulty incrementally, which is crucial in environments requiring adaptive exploration strategies.

Implications and Future Directions

HiER's novel approach to experience replay offers theoretical and practical advantages, addressing one of the significant barriers in RL, the challenge of sparse rewards. By emphasizing critical experiences, the technique aligns with natural learning methods, potentially informing future explorations into biologically-inspired RL models. Meanwhile, the curriculum learning method, E2H-ISE, reinforces the importance of structured learning pathways in RL, suggesting further investigation into various entropy management strategies.

Future research could explore optimizing the adaptive parameters within both HiER and E2H-ISE to better tailor them to specific environments or tasks. Additionally, the application of HiER+ in transfer learning scenarios, such as sim-to-real transitions in robotics, could provide further insights into its practical utility and efficacy in reducing the gap between simulated and real-world environments. This avenue presents substantial opportunities for advancing the field of autonomous robotic systems and other domains where RL is used to manage complex decision-making processes.