A Survey of Imitation Learning Methods, Environments and Metrics (2404.19456v2)

Abstract: Imitation learning is an approach in which an agent learns how to execute a task by trying to mimic how one or more teachers perform it. This learning approach offers a compromise between the time it takes to learn a new task and the effort needed to collect teacher samples for the agent. It achieves this by balancing learning from the teacher, who has some information on how to perform the task, and deviating from their examples when necessary, such as states not present in the teacher samples. Consequently, the field of imitation learning has received much attention from researchers in recent years, resulting in many new methods and applications. However, with this increase in published work and past surveys focusing mainly on methodology, a lack of standardisation became more prominent in the field. This non-standardisation is evident in the use of environments, which appear in no more than two works, and evaluation processes, such as qualitative analysis, that have become rare in current literature. In this survey, we systematically review current imitation learning literature and present our findings by (i) classifying imitation learning techniques, environments and metrics by introducing novel taxonomies; (ii) reflecting on main problems from the literature; and (iii) presenting challenges and future directions for researchers.

• The paper formalizes essential MDP components and policy mappings to underpin agent-environment interactions in sequential decision-making.
• The paper demonstrates how teacher-generated trajectories and behavior cloning enable efficient transfer of policy knowledge.
• The paper outlines the roles of experiences and observations in reinforcement learning, paving the way for enhanced agent performance and adaptive control.

An Examination of Definitions in Sequential Decision Making Frameworks

In this paper, the authors provide a formalization of essential constructs within the framework of Markov Decision Processes (MDPs), policies, agents, trajectories, demonstrations, experiences, and observations, which are central to the paper of sequential decision-making and reinforcement learning (RL).

The foundational element herein is the Markov Decision Process, defined as a five-tuple $M = \langle S, A, T, r, \gamma \rangle$. An MDP encapsulates the environment in which an agent operates, characterized by the state space $S$, the action space $A$, the transition dynamics $T$, the immediate reward function $r$, and the discount factor $\gamma$. These components collectively govern the stochastic process through which future states are determined based on current actions.

A policy $\pi$ plays a pivotal role by mapping states to actions, essentially directing the agent's behavior. It is parameterized by a set $\theta$, which comprises internal variables adjusted during learning to optimize decision-making strategies within $S \to A$.

Agents interact with their environment by selecting actions $a$ from the action space $A$ in response to states $s$ from the state space $S$. This action selection establishes a sequence of states and actions forming a trajectory $$\tau = \left [ (s_1, a_1), (s_2, a_2), \dots, (s_n, a_n) \right ]$$. Trajectories are instrumental for analyzing agent behaviors and for training in imitation learning paradigms.

The concept of a teacher is introduced as a particular instantiation of an agent, from which demonstrations are sampled. These demonstrations—state-action pairs from a teacher's trajectory—are critical for behavior cloning, allowing the transfer of policy knowledge. The probability distribution of these demonstrations provides insights into the frequency and likelihood of specific state-action combinations.

Experiences, defined as $(s, a, s')$ tuples derived from learned agent trajectories, and observations, defined as state pairs $(s, s')$, capture interactions with the environment. They serve different roles in model-based RL where predicting subsequent states or accessing unannotated transitions is crucial.

The paper thus lays a structured foundation for further exploration and development in the field. The formal definitions and methods elucidate the relationships and distinctions between these constructs. Advanced RL frameworks and algorithms can build on these definitions by leveraging the structured data to enhance agent performance, policy optimization, and behavioral adaptation.

The implications of this formalization are profound in practical applications, with potential advancements in AI systems, automated control processes, and adaptive technologies. Theoretical development could foreseeably lead to richer modeling approaches and more sophisticated learning paradigms. Future research may explore integrating these constructs with neural network-based function approximation methods, further paving the way toward more autonomous and intelligent decision-making systems.