Preferences Implicit in the State of the World (1902.04198v2)

Abstract: Reinforcement learning (RL) agents optimize only the features specified in a reward function and are indifferent to anything left out inadvertently. This means that we must not only specify what to do, but also the much larger space of what not to do. It is easy to forget these preferences, since these preferences are already satisfied in our environment. This motivates our key insight: when a robot is deployed in an environment that humans act in, the state of the environment is already optimized for what humans want. We can therefore use this implicit preference information from the state to fill in the blanks. We develop an algorithm based on Maximum Causal Entropy IRL and use it to evaluate the idea in a suite of proof-of-concept environments designed to show its properties. We find that information from the initial state can be used to infer both side effects that should be avoided as well as preferences for how the environment should be organized. Our code can be found at https://github.com/HumanCompatibleAI/rlsp.

Preferences Implicit in the State of the World: A Formal Analysis

The paper, "Preferences Implicit in the State of the World," provides a nuanced approach to reinforcement learning (RL) through the introduction of an implicit preference inference mechanism. The authors propose that the state of an environment already reflects human optimization preferences, which can be harnessed by autonomous agents to infer both explicit and implicit task-specific details that are crucial for effectively achieving human-aligned outcomes. Central to the paper is the hypothesis that human preferences can be derived from the observed state of an environment, offering an alternative to specifying entire reward functions manually or learning from human demonstrations.

Core Contributions

The research presented addresses several notable issues in reinforcement learning, particularly the challenge of designing reward functions that fully encapsulate human desires and preferences. The primary insights and contributions of the paper are:

1. State-Based Preference Inference: When an RL agent is introduced into an environment optimized by human agents, the state conveys implicit information about human preferences. By analyzing the initial state, the agent can infer constraints and objectives that were possibly not made explicit.
2. Reward Learning Through Maximum Causal Entropy IRL: The authors utilize Maximum Causal Entropy Inverse Reinforcement Learning (MCEIRL) to formulate a framework where the reward functions are learned directly from the state of the environment rather than from traditional model-based methods or human demonstrations. A notable result is the derivation of reward functions from just a single state snapshot, as opposed to a sequence of states or actions.
3. Evaluation within Proof-of-Concept Environments: The introduction of an algorithm, Reward Learning by Simulating the Past (RLSP), is evaluated in a suite of synthetic environments, designed to highlight its ability to deduce correct reward structures and to avoid unintended side-effects.
4. Algorithm Robustness and Trade-off with Specified Rewards: The paper examines the algorithm's performance through variations in both initial knowledge of the environment and the RL agents' planning horizon, investigating the robustness of inferred preferences and its practical applications.

Implications and Future Directions

The methodology presented has several tangible implications. By relying on environmental states as repositories of preference information, RL agents can autonomously align their actions with nuanced human desires without exhaustive enumeration of reward criteria by their human operators. This marks a significant adjustment from reward design being an expert-driven process to one of leveraging environmental embeddings of preferences.

The recognition that RL agents can independently infer implicit goals expands their applicability in complex, real-world scenarios where inverse relationships between actions and goals are not clearly defined. Consequently, scaling this approach to large, dynamic environments would greatly enhance the feasibility of deploying RL systems with minimal human intervention.

Looking forward, further exploration is needed to address the challenge posed by dynamic and non-static environments, particularly how the RLSP framework adapts when faced with unknown transition dynamics and nonlinear reward setups. Additionally, optimizing the trade-off between inferred preferences and explicitly set task rewards is a crucial area of research; better techniques are needed to prioritize frame conditions over task-specific incentives, ensuring that agents’ actions remain consistent with human intentions even when those intentions are underspecified.

Conclusion

While the paper provides insightful contributions to preference-based learning in RL, many of the assumptions, such as static environments and known dynamics, present limitations that need empirical trial in more realistic settings. Future studies should aim to relax these constraints, enhancing the ability of autonomous agents to operate in environments characterized by high degrees of uncertainty and unpredictability. The paper effectively lays the groundwork for furthering our understanding of preference learning and its utility in creating truly autonomous systems aligned with human values.