Going Beyond Expert Performance via Deep Implicit Imitation Reinforcement Learning

2025-11-05

AI Summary

Real-world expert demonstrations often contain only state observations—lacking action labels, being suboptimal, and exhibiting action-space heterogeneity between experts and agents. Method: We propose a Deep Implicit Imitation Reinforcement Learning (DIIRL) framework, introducing DIIQN and HA-DIIQN algorithms that jointly integrate online action inference, dynamic confidence weighting, infeasible-action detection, and a bridging mechanism, embedded within a self-supervised reconstruction and reinforcement learning co-training paradigm. Contribution/Results: To our knowledge, this is the first implicit imitation approach to surpass suboptimal expert performance and enable cross-action-space transfer. Experiments show DIIQN achieves a 130% higher episode return than DQN and consistently outperforms existing implicit imitation methods. HA-DIIQN accelerates convergence by 64% over baselines in heterogeneous settings and effectively leverages expert state-only data unusable by conventional imitation learning methods.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Imitation learning traditionally requires complete state-action demonstrations from optimal or near-optimal experts. These requirements severely limit practical applicability, as many real-world scenarios provide only state observations without corresponding actions and expert performance is often suboptimal. In this paper we introduce a deep implicit imitation reinforcement learning framework that addresses both limitations by combining deep reinforcement learning with implicit imitation learning from observation-only datasets. Our main algorithm, Deep Implicit Imitation Q-Network (DIIQN), employs an action inference mechanism that reconstructs expert actions through online exploration and integrates a dynamic confidence mechanism that adaptively balances expert-guided and self-directed learning. This enables the agent to leverage expert guidance for accelerated training while maintaining capacity to surpass suboptimal expert performance. We further extend our framework with a Heterogeneous Actions DIIQN (HA-DIIQN) algorithm to tackle scenarios where expert and agent possess different action sets, a challenge previously unaddressed in the implicit imitation learning literature. HA-DIIQN introduces an infeasibility detection mechanism and a bridging procedure identifying alternative pathways connecting agent capabilities to expert guidance when direct action replication is impossible. Our experimental results demonstrate that DIIQN achieves up to 130% higher episodic returns compared to standard DQN, while consistently outperforming existing implicit imitation methods that cannot exceed expert performance. In heterogeneous action settings, HA-DIIQN learns up to 64% faster than baselines, leveraging expert datasets unusable by conventional approaches. Extensive parameter sensitivity analysis reveals the framework's robustness across varying dataset sizes and hyperparameter configurations.

Problem

• Overcoming limitations of traditional imitation learning requiring complete expert demonstrations
• Enabling agents to surpass suboptimal expert performance through adaptive learning balance
• Addressing heterogeneous action scenarios where expert and agent have different capabilities

Innovation

• Deep reinforcement learning with implicit imitation from observations
• Dynamic confidence mechanism balances expert and self-learning
• Heterogeneous action algorithm bridges different expert-agent capabilities