TLDR: Heuristic Enhanced Policy Optimization (HEPO) is a novel reinforcement learning framework that effectively leverages human-designed heuristic rewards by imposing a constraint that ensures the learned policy improves upon a heuristic-only baseline. This approach mitigates reward hacking and reduces the need for extensive manual reward tuning, even performing well with sub-optimally designed heuristics, as demonstrated across various robotic tasks.
Reinforcement Learning (RL) has shown incredible potential in enabling AI to master complex tasks, often surpassing human capabilities. However, a common challenge in RL is dealing with sparse or delayed task rewards, which can make training policies inefficient. To overcome this, human-designed ‘heuristic rewards’ are often introduced. These heuristics provide denser, more immediate feedback, guiding the agent towards desired behaviors.
While heuristics are beneficial, they come with a significant drawback: they can introduce a bias, leading to ‘reward hacking.’ This is where an RL algorithm finds unintended ways to maximize the heuristic reward without truly achieving the original task objective. For instance, a robot designed to walk fast might be given a heuristic reward for walking like a human. This could prevent it from discovering faster, non-human-like gaits. Traditionally, balancing task rewards and heuristic rewards involves extensive manual tuning of a weighting parameter, which is both tedious and computationally expensive.
Existing theoretical approaches to incorporate heuristics often rely on ‘policy invariance,’ aiming to ensure that a policy optimized with heuristics performs the same as one optimized solely on the task reward. However, practical results have shown that these methods often perform poorly, sometimes even worse than simply optimizing the heuristic reward alone.
A new framework, Heuristic Enhanced Policy Optimization (HEPO), proposes a different approach. Instead of focusing on policy invariance, HEPO prioritizes ‘policy improvement.’ The core idea is to train a policy that not only uses heuristic rewards but is also guaranteed to perform at least as well as a policy trained only with heuristics. This is achieved by imposing a constraint during the optimization process: the task performance of the learned policy must be greater than or equal to that of the heuristic policy.
HEPO formulates this as a constrained optimization problem, which is then converted into an unconstrained min-max problem using Lagrangian duality. This allows the algorithm to adaptively adjust the importance of the task reward based on how well the policy is performing relative to the heuristic baseline. When the learned policy outperforms the heuristic policy, the emphasis on the task reward decreases; conversely, if it falls behind, the task reward’s importance increases.
The researchers implemented HEPO using Proximal Policy Optimization (PPO), a widely used RL algorithm. A key aspect of HEPO’s implementation is the concurrent training of both the enhanced policy and the heuristic policy, allowing them to share data and improve sample efficiency. This avoids the need for pre-training the heuristic policy, simplifying the overall process.
Experiments were conducted on 29 challenging robotic tasks from IsaacGym and Bidexterous Manipulation benchmarks, which typically rely on carefully engineered reward functions. HEPO consistently outperformed other methods, including policies trained only with task rewards (J-only), only with heuristic rewards (H-only), a mixture of both (J+H), and other principled methods like Potential-based Reward Shaping (PBRS) and Heuristic-guided Reinforcement Learning (HuRL). HEPO showed a statistically significant probability of improving upon the heuristic-only policy.
Perhaps even more surprisingly, HEPO demonstrated its robustness when trained with heuristic reward functions designed by non-expert human participants. Even with poorly engineered heuristics, HEPO managed to achieve good performance, often surpassing PPO trained with the same sub-optimal heuristics. This suggests HEPO’s potential to significantly reduce the human effort required in designing effective reward functions for RL tasks.
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Ablation studies further highlighted HEPO’s design choices, showing that using the heuristic policy as a reference for the improvement constraint and collecting trajectories jointly from both policies lead to superior performance compared to alternative strategies. While HEPO does not theoretically guarantee convergence to the optimal policy, its empirical performance and ability to leverage even imperfect heuristics make it a promising plug-and-play optimization method for reinforcement learning practitioners. For more technical details, you can refer to the full research paper.
