Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning (2010.03950v2)

Abstract: Reinforcement learning (RL) methods usually treat reward functions as black boxes. As such, these methods must extensively interact with the environment in order to discover rewards and optimal policies. In most RL applications, however, users have to program the reward function and, hence, there is the opportunity to make the reward function visible -- to show the reward function's code to the RL agent so it can exploit the function's internal structure to learn optimal policies in a more sample efficient manner. In this paper, we show how to accomplish this idea in two steps. First, we propose reward machines, a type of finite state machine that supports the specification of reward functions while exposing reward function structure. We then describe different methodologies to exploit this structure to support learning, including automated reward shaping, task decomposition, and counterfactual reasoning with off-policy learning. Experiments on tabular and continuous domains, across different tasks and RL agents, show the benefits of exploiting reward structure with respect to sample efficiency and the quality of resultant policies. Finally, by virtue of being a form of finite state machine, reward machines have the expressive power of a regular language and as such support loops, sequences and conditionals, as well as the expression of temporally extended properties typical of linear temporal logic and non-Markovian reward specification.

Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning

The paper "Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning" presents an innovative approach to improving the sample efficiency of reinforcement learning (RL) by utilizing internal reward function structures. Traditional RL techniques often treat reward functions as opaque entities, demanding extensive environmental interactions for learning optimal policies. However, the authors introduce a mechanism called reward machines (RM), a type of finite state machine, that makes the reward function's structure accessible to the RL agent. By doing so, agents can learn optimal policies more efficiently.

Key Contributions

1. Reward Machines: The authors debut reward machines, which capture the structure of reward functions. These machines can represent complex functions through finite states, allowing for specifications that include sequences, conditionals, and loops. Importantly, reward machines have the expressive power of regular languages, thereby supporting temporally extended and non-Markovian reward specifications.
2. Exploiting Reward Structure: The paper details methodologies for leveraging reward machine structures in RL, including:
   • Automated Reward Shaping: This method employs potential-based reward shaping based on state evaluations from the reward machine, guiding agents toward task completion more directly.
   • Counterfactual Reasoning: By generating synthetic experiences based on the current RM state and possible transitions, agents can learn more efficiently, akin to off-policy learning.
   • Hierarchical RL for Reward Machines: HRM decomposes tasks into smaller, manageable options aligned with RM states, facilitating quicker policy learning.

Experiments and Results

The authors conducted extensive experiments across various domains, including tabular environments, discrete domains, continuous state spaces, and continuous control tasks. The results consistently demonstrate that exploiting reward structure significantly improves sample efficiency and policy quality when compared to traditional cross-product RL methods. In particular, the counterfactual reasoning (CRM) approach consistently yielded strong performance, often converging to optimal policies in tabular domains. Hierarchical methods (HRM) generally learned policies more rapidly but sometimes settled on suboptimal solutions due to inherent limitations in option-based frameworks.

Implications and Future Directions

This research suggests practical advantages in exposing reward structures to RL agents, potentially reducing computation costs associated with environmental interactions. The ability to specify tasks using regular languages within the RM framework provides a versatile mechanism applicable across diverse RL settings, including multitask learning.

The introduction of reward machines opens several avenues for future research, such as integrating RMs with model-based RL methods, addressing the challenges of noisy environments, and extending the framework to support unseen task generalization. Additionally, RMs present opportunities to bridge RL with formal language specifications, promoting robust and transparent task definition and execution. As the field progresses, leveraging reward machines in conjunction with inverse reinforcement learning could enhance reward function design further, aligning learning objectives more closely with intended task outcomes.

Overall, the paper effectively expands on existing RL paradigms by proposing a structured and semantically rich approach to reward specification and exploitation, setting a foundation for subsequent innovations in AI learning methodologies.