Accelerating Reinforcement Learning through Implicit Imitation

Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments an agent’s ability to learn useful behaviors by making intelligent use of the knowledge implicit in behaviors demonstrated by cooperative teachers or other more experienced agents. We propose and study a formal model of implicit imitation that can accelerate reinforcement learning dramatically in certain cases. Roughly, by observing a mentor, a reinforcement-learning agent can extract information about its own capabilities in, and the relative value of, unvisited parts of the state space. We study two specific instantiations of this model, one in which the learning agent and the mentor have identical abilities, and one designed to deal with agents and mentors with different action sets. We illustrate the benefits of implicit imitation by integrating it with prioritized sweeping, and demonstrating improved performance and convergence through observation of single and multiple mentors. Though we make some stringent assumptions regarding observability and possible interactions, we briefly comment on extensions of the model that relax these restricitions.

💡 Research Summary

The paper introduces a novel framework called “implicit imitation” that accelerates reinforcement learning (RL) by allowing an agent to learn from the observed state transitions of a more experienced mentor without requiring explicit instruction or shared reward signals. The authors first review the standard Markov Decision Process (MDP) formulation, value iteration, and model‑based RL, establishing the groundwork for their approach.

In the implicit imitation setting, the learner continuously watches the mentor’s interactions with the environment, recording tuples of the form (s, s′) that represent state transitions caused by the mentor’s (unknown) actions. By incorporating these observations into its own estimated transition model, the learner can perform “augmented Bellman backups,” updating its value function much more rapidly than with self‑generated experience alone.

Two concrete instantiations are presented. In the homogeneous case, the learner and mentor share the same action set; thus each observed transition can be directly mapped to a learner action, and the learner’s model is updated accordingly. The authors integrate this mechanism with prioritized sweeping, giving high priority to states frequently visited by the mentor, which focuses exploration on promising regions of the state space.

The heterogeneous case handles differing action sets. Here, the learner cannot simply copy the mentor’s transition. The paper introduces feasibility testing to determine whether a mentor’s transition can be reproduced with the learner’s actions. When it cannot, a “k‑step repair” procedure constructs a short sequence of learner actions that approximates the mentor’s trajectory, allowing the learner to still benefit from the mentor’s guidance while avoiding misleading information.

Additional contributions include an attention‑focusing scheme that biases exploration toward mentor‑frequented high‑value areas, and a discussion of how multiple mentors can be leveraged simultaneously, effectively distributing the search effort across several sources of expertise.

Empirical evaluation on several navigation domains demonstrates that implicit imitation dramatically reduces the number of learning episodes required for convergence and often yields higher‑quality policies compared with standard prioritized sweeping. The benefits are especially pronounced when multiple mentors are available, as their combined observations provide richer guidance.

While the core model assumes full observability, shared reward functions, and known state‑space mappings, the authors outline extensions to partially observable settings, non‑shared rewards, and uncooperative mentors, suggesting a broad applicability of implicit imitation to real‑world multi‑agent systems where explicit teaching is impractical. Overall, the work positions implicit imitation as a powerful, low‑overhead method for boosting RL performance by passively exploiting the latent knowledge embedded in the behavior of more capable agents.