HiER+: Enhanced Off-Policy Reinforcement Learning

• HiER+ is a reinforcement learning approach that integrates highlight experience replay with curriculum learning to enhance data efficiency in complex robotic tasks.
• The method employs a highlight buffer to selectively store high-quality episodes based on cumulative rewards, ensuring robust learning signals.
• By gradually increasing initial state entropy, HiER+ scaffolds exploration and improves policy robustness, leading to significantly higher success rates in off-policy environments.

HiER+ designates a combined reinforcement learning methodology that integrates "Highlight Experience Replay" (HiER) with curriculum learning based on initial state entropy modulation. In this approach, advanced experience selection and a controlled curriculum of data collection are systematically fused to enhance the performance and sample efficiency of off-policy RL agents, particularly in continuous-state, sparse-reward, real-world robotic tasks. HiER+ achieves this by introducing an auxiliary replay buffer for ‘highlight’ episodes (those deemed particularly informative based on episode reward), in conjunction with a dynamic curriculum that gradually increases the entropy (diversity and difficulty) of the initial state distribution, thereby scaffolding exploration and policy robustness.

1. Structural Components of HiER+

HiER+ is composed of two primary mechanisms:

• Highlight Experience Replay (HiER): Maintains a secondary buffer ("highlight buffer," $B_{hier}$) to store complete episodes that exceed a given performance threshold $\lambda$, measured by the undiscounted cumulative episode reward $R = \sum_t r_t$. This buffer functions in parallel with the standard replay buffer ($B_{ser}$), and is designed to preferentially amplify effective learning signals by emulating an automatic, self-generated demonstration process.
• Easy2Hard Initial State Entropy (E2H-ISE): A curriculum-learning module that systematically modulates the entropy parameter $c$ of the initial state–goal distribution $\mu_0$. This mechanism enables an "easy-to-hard" progression: training starts on deterministic or low-entropy initial states ($c=0$), and $c$ is gradually increased to diversify episode initializations, driving the agent toward more challenging and generalizable behavior.

Integration is realized at both data collection and policy update stages, where episode generation is governed by the current curriculum ($\mu_0^c$), and buffer sampling at update time is balanced via a dynamic mixing ratio $\xi$.

2. Detailed HiER+ Algorithmic Workflow

Experience Storage and Buffer Management:

• Every transition is appended to $B_{ser}$.
• Full episodes with $R > \lambda$ are appended to $B_{hier}$ as well.
• The threshold $\lambda$ may be set via linear profiling, $\lambda_j = \min(1, t / (T_{total} \cdot z_{sat}))$ (where $t$ is the timestep and $z_{sat}$ the desired saturation time), or adapted with a moving average.

Policy Update:

• During gradient updates, samples are drawn from both buffers in proportion to a priority metric driven by TD-error magnitudes:

$$\xi_k = \frac{L_{hier, k}^{\alpha_p}}{L_{hier, k}^{\alpha_p} + L_{ser, k}^{\alpha_p}}$$

where $L_{hier, k}$ and $L_{ser, k}$ are mean TD-errors for samples from $B_{hier}$ and $B_{ser}$, respectively, and $\alpha_p$ is a prioritization exponent.

Data Collection Curriculum (E2H-ISE):

• Entropy $c$ of the start-state distribution is modulated with predefined, self-paced, or feedback-control updates:
  • Linear/predefined: $c$ increases linearly/saturates over a given number of steps.
  • Self-paced: $c$ increases or decreases based on observed training success rates ($P_{train, w}$) compared to bounds ($\Psi_{low}$, $\Psi_{high}$).
  • Feedback control: $c$ is incremented or decremented to maintain a specified target success rate $\psi$.

This dual mechanism ensures that highlighted, high-value experiences contribute maximally to policy gradient steps, while the agent incrementally faces more challenging state distributions during training.

3. Integration with Off-Policy RL

HiER+ is agnostic to the underlying RL algorithm and has been validated with Soft Actor-Critic (SAC) as well as TD3 and DDPG. It is compatible with:

• Hindsight Experience Replay (HER): Experience relabelling for sparse-reward scenarios.
• Prioritized Experience Replay (PER): Sampling transitions proportional to TD-error magnitude.

Buffer orchestration and update scheduling can be tuned to favor highlight samples, standard replay, or a mixture thereof, depending on observed learning dynamics.

4. Empirical Results and Quantitative Improvements

Extensive evaluation was conducted on the panda-gym platform featuring three representative robotic manipulation tasks (push, slide, pick-and-place), which are characterized by high-dimensional action/state spaces and sparse rewards.

• Performance Metrics: The method reports mean best success rates of 1.0 (push), 0.83 (slide), 0.69 (pick-and-place) with HiER+ (HER active, PER inactive), substantially higher than the respective baselines.
• Comparative Analysis: Each addition (HiER buffer, E2H-ISE curriculum, HER/PER) individually increases success rate, but their combination ("HiER+") consistently outperforms vanilla variants.
• Sample Efficiency: HiER+ accelerates convergence, achieving a higher success rate faster and with fewer samples. This is corroborated in Figure 1 and Table 1 of the source, which report both efficiency and final performance.

A plausible implication is that by cascading curriculum “difficulty” and buffer “quality,” HiER+ can mitigate both exploration and credit assignment bottlenecks typical in high-dimensional, sparse-reward RL.

5. Applicability, Modularity, and Limitations

Domains and Use Cases:

• HiER+ is tailored to applications where access to human demonstrations is limited or impractical, initial state variability is high, and rewards are sparse (e.g., real-world robotic manipulation, continuous control).

Integration Flexibility:

• The methodology is modular: both HiER and E2H-ISE components can be toggled or adapted for target environments.
• Parameter tuning (threshold $\lambda$, mixing ratio $\xi$, curriculum step size) is required and must be aligned to environmental scale and reward density.

Practical Limitations:

• Effectiveness is sensitive to the setting and schedule of $\lambda$ and $c$; misconfiguration may bias the agent toward trivial solutions or overly difficult starts.
• The highlight buffer may become dominated by repetitive episodes if the performance threshold is not adequately varied or if task reward structures are poorly shaped.
• In sim-to-real settings, further investigation is needed for robust transfer, as the curation of “highlight” experiences may be environment-specific.

6. Implications and Future Directions

HiER+ demonstrates that the combination of curriculum-guided data collection and prioritized replay of high-value experience enables agents to efficiently bootstrap performance from sparse signals, even without expert demonstrations. The methodology is expected to lower the sample complexity and accelerate policy robustness in large-scale or high-variance settings.

A plausible implication is that HiER+ may facilitate sim-to-real transfer in robotics and other practical applications where collecting diverse, high-quality demonstrations is prohibitive. Further development of dynamically adaptive thresholding or curriculum strategies could further generalize HiER+'s applicability to open-ended or evolving environments.

7. Summary Table: HiER+ Components and Roles

Component	Mechanism	Purpose
HiER	Highlight buffer + reward-based gating	Amplifies high-quality samples
E2H-ISE	Curriculum over initial state entropy	Scaffolds exploration
HER, PER	Optional replay strategies	Sparse-reward relabeling/prioritization
$\xi$ mixing	Priority-driven update ratio	Balances exploitation/exploration
$\lambda$, $c$	Reward and curriculum thresholds	Control episode selection and diversity

HiER+ exemplifies an overview of replay-based experience curation and adaptive state–goal curriculum, each proven to enhance data efficiency, and demonstrates that such integration is effective for complex off-policy reinforcement learning in robotics and related fields (Horváth et al., 2023).