Going Beyond Expert Performance via Deep Implicit Imitation Reinforcement Learning

• URL: http://arxiv.org/abs/2511.03616v1
• Date: Wed, 05 Nov 2025 16:33:39 GMT
• Title: Going Beyond Expert Performance via Deep Implicit Imitation Reinforcement Learning
• Authors: Iason Chrysomallis, Georgios Chalkiadakis,
• Abstract summary: We introduce a deep implicit imitation reinforcement learning framework that combines deep reinforcement learning with implicit imitation learning from observation-only datasets.<n>Our main algorithm, Deep Implicit Q-Network (DIIQN), employs an action inference mechanism that reconstructs expert actions through online exploration.<n>We further extend our framework with a Heterogeneous Actions DIIQN (HA-DIIQN) algorithm to tackle scenarios where expert and agent possess different action sets.
• Score: 3.691573844585973
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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