TLDR: RLAF is a novel reinforcement learning framework that addresses the challenge of complex, history-dependent reward structures by replacing explicit reward functions with preferences derived from a deterministic finite automaton (DFA). It uses DFA-based scoring (subtask completion or transition values, potentially with transfer learning) to generate preferences, which then train a reward function via pairwise ranking loss. The framework offers static and dynamic learning variants and has been empirically shown to outperform traditional methods in various discrete and continuous environments, providing a scalable and human-independent approach to non-Markovian reinforcement learning.
Reinforcement Learning (RL) has achieved remarkable success in various fields, from game playing to robotics. However, a significant hurdle remains: designing effective reward functions, especially in environments where desired outcomes depend on long-term sequences of actions or complex historical events. This challenge, known as handling non-Markovian reward structures, often requires painstaking manual reward engineering, which can be labor-intensive and prone to bias.
A new research paper titled “RLAF: Reinforcement Learning from Automaton Feedback” introduces a novel approach to tackle this problem. Authored by Mahyar Alinejad, Alvaro Velasquez, Yue Wang, and George Atia, this work proposes a framework that replaces explicit reward functions with preferences derived from a deterministic finite automaton (DFA). This innovative method eliminates the need for manual reward engineering, offering a scalable, efficient, and human-independent alternative to traditional reward modeling.
The core idea behind RLAF is to leverage the structure of a DFA to automatically generate preferences over different trajectories an RL agent might take. Unlike conventional methods that use automata to directly specify numerical rewards, RLAF uses the DFA to rank which trajectories are closer to satisfying a given task specification. This ordinal signal is then used to learn a reward function, which in turn guides the agent’s policy optimization.
How RLAF Works: Generating Preferences and Learning Rewards
The framework introduces two distinct approaches for generating these crucial preference scores:
- Subtask-based scoring: This method evaluates trajectories based on two factors: the number of subgoals completed in the correct order (as tracked by the DFA) and the distance to the next required subgoal or the final goal. Trajectories that complete more subgoals and are closer to the next objective receive higher scores.
- Automaton transition value-based scoring: Here, the system integrates ‘automaton transition values’ into the trajectory scoring. These values represent the desirability of specific transitions within the DFA and can be derived during training or obtained through a transfer learning process from a simpler ‘teacher’ environment. By summing these values along a trajectory, the method quantifies its cumulative desirability. This approach is particularly beneficial for transferring knowledge across environments that share the same logical structure.
Once preferences are generated, RLAF learns a reward function using a pairwise ranking loss. This ensures that trajectories preferred by the DFA receive higher cumulative rewards, effectively embedding the task’s temporal structure into the reward model without direct human intervention.
Static and Dynamic Learning Variants
RLAF offers two modes of operation:
- Static version: The reward function is learned once from an initial set of preferences. The policy is then optimized using this fixed reward function. This is efficient for tasks with stable reward structures.
- Dynamic version: The reward function and policy are iteratively refined. Trajectories from the current policy are evaluated by the DFA to produce new preferences, which continuously update the reward function and policy until convergence. This variant is well-suited for complex tasks requiring ongoing refinement.
Empirical Validation and Key Advantages
The researchers rigorously evaluated RLAF in both discrete and continuous environments, including gridworlds (like Minecraft Iron Sword Quest, Dungeon Quest, Blind Craftsman, Minecraft Building Bridge), a physics-based navigation task (Mountain Car Collection), and a high-dimensional continuous control problem (Warehouse Robotics). The results consistently demonstrated that RLAF enables RL agents to learn effective policies for tasks with temporal dependencies, outperforming traditional reward engineering and automaton-based baselines such as reward machines and LTL-guided methods.
A significant finding was the effectiveness of combining subtask-based preferences with transition-value-based knowledge transferred from a teacher agent. This distillation process significantly enhanced learning efficiency in more complex ‘student’ environments, leading to faster convergence and better performance.
The paper also provides a theoretical convergence guarantee, showing that under standard assumptions, the automaton-guided preference-based framework learns a policy that is near-optimal with respect to the true non-Markovian objective. For more technical details, you can read the full paper here.
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Looking Ahead
While RLAF presents a robust solution, the authors acknowledge certain limitations. Constructing DFAs for very large-scale environments can be challenging, and the current preference generation heuristics might need enrichment for extremely complex domains. However, the framework’s ability to generalize to continuous and high-dimensional domains, coupled with its theoretical underpinnings, positions it as a promising direction for future research in reinforcement learning.
