Expert Behavior Prior Reinforcement Learning

Expert Behavior Prior Reinforcement Learning | AI Research

Key Takeaways

Expert Behavior Prior Reinforcement Learning This paper introduces the Expert Behavior Prior (EBP) algorithm, a new approach designed to improve the efficien...
Behavior prior reinforcement learning (BPRL) has emerged as a promising paradigm to improve sample efficiency in online reinforcement learning (RL) by leveraging policy priors derived from offline demonstrations.
However, most existing BPRL methods rely on static offline datasets, which often suffer from low data diversity and suboptimal trajectory quality.
This reliance restricts the effectiveness of policy priors, hindering both policy exploitation and stability during online training.
Consequently, agents are prone to inefficient exploration and unstable learning dynamics.

Paper AbstractExpand

Behavior prior reinforcement learning (BPRL) has emerged as a promising paradigm to improve sample efficiency in online reinforcement learning (RL) by leveraging policy priors derived from offline demonstrations. However, most existing BPRL methods rely on static offline datasets, which often suffer from low data diversity and suboptimal trajectory quality. This reliance restricts the effectiveness of policy priors, hindering both policy exploitation and stability during online training. Consequently, agents are prone to inefficient exploration and unstable learning dynamics. To address these limitations, we deviate from existing offline pre-training methods and propose an Expert Behavior Prior (EBP) algorithm. Specifically, we introduce a Q-guided conditional variational autoencoder (Q-CVAE) that learns to generate expert policy priors directly from the online replay buffer. This enables the generation of high-value actions for guiding policy updates without relying on pre-collected expert trajectories. To further enhance policy exploitation, we propose an expert policy guidance (EPG) mechanism that selects expert actions from a generative support set, and we integrate a policy gradient correction (PGC) module to harmonize Q-guidance with expert supervision, promoting stable and consistent policy improvement. Extensive experiments conducted on robotic control (Gym, PyBullet) and industrial control (DMControl) benchmarks demonstrate that EBP significantly outperforms state-of-the-art online RL algorithms, achieving higher sample efficiency and more stable convergence.

Expert Behavior Prior Reinforcement Learning
This paper introduces the Expert Behavior Prior (EBP) algorithm, a new approach designed to improve the efficiency and stability of online reinforcement learning (RL). Traditional RL agents often struggle with sample inefficiency and unstable learning because they rely on inaccurate value estimates or suboptimal pre-collected data. EBP addresses these issues by enabling the agent to generate its own high-quality "expert" guidance directly from its ongoing experience, eliminating the need for static, pre-existing datasets.

Generating Expert Guidance Online

Instead of relying on fixed offline datasets, which often lack diversity, EBP uses a Q-guided conditional variational autoencoder (Q-CVAE). This model learns to generate high-value actions by observing the agent's own performance in the online replay buffer. By combining a reconstruction loss—which ensures the actions are realistic—with a Q-guided loss—which pushes the model toward higher-reward actions—the system creates a dynamic, self-improving source of expert knowledge that evolves alongside the agent.

Stabilizing Policy Updates

To ensure that this generated guidance actually improves the agent, the paper introduces an Expert Policy Guidance (EPG) mechanism and a Policy Gradient Correction (PGC) module. The EPG mechanism creates a "support set" of potential actions from the generative model and selects the one with the highest predicted value. The PGC module then acts as a balancer, harmonizing the guidance from the Q-function with the expert supervision from the generative model. This dual-input approach helps the agent avoid the common pitfalls of erratic policy shifts and inefficient exploration.

Performance and Reliability

The authors tested EBP across several challenging benchmarks, including robotic control (Gym, PyBullet) and industrial control (DMControl). The results indicate that EBP consistently outperforms state-of-the-art online RL algorithms in terms of sample efficiency and convergence stability. Furthermore, the researchers demonstrated that the algorithm remains robust even when exposed to noisy environments, suggesting that the integration of generative priors provides a reliable framework for training agents in complex, real-world scenarios.

Key Considerations

While EBP shows significant promise, the authors note that its effectiveness relies on the quality of the Q-function's guidance. In environments where exploration is extremely limited or rewards are sparse, the agent may still struggle to find the optimal path, as seen in their diagnostic "ToyEnv" experiments. Additionally, the performance of the system is influenced by the weight assigned to the Q-guided loss, which requires careful tuning to ensure the generative model remains both diverse and high-performing.