Interactive Reinforcement Learning Overview

Updated 26 December 2025

Interactive Reinforcement Learning is an extension of reinforcement learning that incorporates external feedback to shape and accelerate policy updates.
It utilizes diverse feedback modalities—such as scalar rewards, action-level advice, and multi-modal signals—to enhance convergence and adaptability.
IRL frameworks have demonstrated improved performance in robotics, feature selection, and human–robot interaction by integrating guided reward adjustments.

Interactive Reinforcement Learning (IRL) is an extension of standard reinforcement learning paradigms in which external feedback—typically provided by a human or an environment-side trainer—directly influences the learner's policy updates, reward structure, or action selection process. Unlike traditional RL, where feedback is derived exclusively from environment-defined reward functions, IRL leverages interactive, often personalized, signals ranging from scalar rewards and action approvals to trajectory scores and multi-modal advice. IRL has been adopted in domains such as robotics, feature selection, and human–robot interaction, allowing agents to acquire complex skills and adaptively align with human objectives via integrated guidance and supplementary feedback mechanisms (Fan et al., 2020, Cruz et al., 2018, Liu et al., 2023, Bora, 16 Jul 2024).

1. Formal Definitions and Architectural Variants

Interactive Reinforcement Learning can be generally summarized as an augmentation of the classical Markov Decision Process (MDP), in which the reward function is the sum of an environment-driven reward and an externally generated feedback signal:

$r(t+1) = r_{env}(t+1) + r_{fb}(t+1)$

where $r_{fb}$ denotes the interactive/human feedback, which may be action-level, state-level, or trajectory-level and can take the form of discrete judgments ( $\{-10, 0, +10\}$ ), binary preferences, or scalar scores (Bora, 16 Jul 2024, Liu et al., 2023). The Bellman optimization objective remains unchanged, but the composite nature of the reward modifies the effective learning trajectory. In multi-agent IRL (e.g., feature selection), each feature-agent operates in a coupled trainer–agent feedback architecture within a multi-agent MDP, allowing for both collective guidance and agent-specific rewards (Fan et al., 2020).

IRL can be instantiated through direct human-in-the-loop acceptance/rejection of actions (Bora, 16 Jul 2024), automated or hybrid trainer interventions (Fan et al., 2020), or full-trajectory scoring with adaptive smoothing (Liu et al., 2023). In multi-modal frameworks, human feedback is synthesized from diverse sensory channels (e.g., audio–visual), and the reliability of each channel is handled through confidence-aware fusion mechanisms (Cruz et al., 2018).

2. Feedback Modalities and Integration Schemes

IRL systems can accept a wide spectrum of feedback signals:

Scalar feedback: Numeric reinforcement values, typically on a fixed scale, are supplied by a trainer at each timestep (Bora, 16 Jul 2024).
Action-level advice: Trainer recommendations may override the agent’s own selection, either deterministically or stochastically (Cruz et al., 2018, Fan et al., 2020).
Trajectory-level scores: Trainers assign quality scores to entire executed trajectories, which are then used to construct dense pairwise preference data for reward model training (Liu et al., 2023).
Multi-modal feedback: Signals from distinct sensory modalities (e.g., speech, gesture) are integrated using confidence estimation and fusion—combining predicted commands via rules that account for incongruence and mutual reinforcement (Cruz et al., 2018).

A principled fusion of these signals typically includes the following algorithmic steps:

Confidence computation for each modality or signal.
Preference for high-confidence modalities and handling of incongruent feedback by boosting or suppressing the composite confidence (e.g., fusion via a “likeliness” parameter φ).
Selective acceptance of feedback based on aggregate confidence thresholds (Cruz et al., 2018).

Table: IRL Feedback Modalities (from primary sources)

Feedback Type	Level	Example Paper
Scalar reward	Action	(Bora, 16 Jul 2024)
Action override	Action	(Cruz et al., 2018)
Trajectory scoring	Trajectory	(Liu et al., 2023)
Multi-modal fusion	Action	(Cruz et al., 2018)

The choice of modality and integration mechanism is strongly application-dependent and influences convergence properties, robustness, and feedback efficiency.

3. Algorithmic Frameworks

Representative IRL algorithms demonstrate significant diversity in learner–trainer interaction, state and reward shaping, and feedback exploitation:

Trainer–Agent Loop (Feature Selection): The trainer is invoked to advise “hesitant” agents based on either K-Best filter (feature ranking) or decision-tree wrapper approaches. Hybrid teaching alternates between these trainers and ultimately yields to agent exploration as training progresses (Fan et al., 2020).
Multi-agent GCN State Embedding: State representations integrate GCN embeddings computed over feature-correlation graphs and decision-tree–derived directed trees. Feature importance from the decision tree modulates the embedding aggregation and provides additional structure to the reward computation (Fan et al., 2020).
Score-to-Preference Reward Shaping: In full-trajectory scoring, all scored trajectories are entered into a buffer. For any trajectory pair, a Bradley–Terry model is used to derive soft preferences, and adaptive label smoothing is employed to buffer against noise in close scores. Reward networks are trained via cross-entropy on these inferred preference labels (Liu et al., 2023).
On/Off-policy IRL Updates: Both off-policy Q-learning and on-policy SARSA can be augmented with human feedback. Empirical evidence supports the superior convergence and policy optimality of off-policy methods when the feedback is reliable and generously provided (Bora, 16 Jul 2024).
Affordance-pruned Exploration: IRL with contextual affordances deploys a neural network to predict the post-action effect and to bypass feedback-driven actions leading to unrecoverable (“failed”) states (Cruz et al., 2018).

4. Reward Design and Feedback Efficiency

Feedback efficiency and appropriate reward shaping are central in IRL. Several approaches have been developed:

Personalized Rewards: Agent-level rewards are functions of feature importance (via DTF), normalized action selection frequency, and task accuracy offset by inter-feature correlation (Fan et al., 2020).
Score-based Feedback Amplification: Scoring n full trajectories enables O(n²⁾ implied preference pairs, making full-trajectory scoring exponentially more data-efficient than strictly pairwise preference querying (Liu et al., 2023).
Adaptive Label Smoothing: For pairs of similarly scored trajectories, adaptive smoothing blends the preference label towards 0.5, reducing sensitivity to random labeling errors and stabilizing reward estimator training under noisy supervision (Liu et al., 2023).
Affordance-based Pruning: Recognition of contextual affordances systematically precludes failing actions, reducing exploration of catastrophic states and accelerating policy learning, especially when combined with multi-modal trainer advice (Cruz et al., 2018).
Feedback Scaling: Use of a fixed feedback range and single-operator curation counteracts inter-rater bias and ensures consistent reward magnitude across training sessions (Bora, 16 Jul 2024).

5. Experimental Evaluations and Benchmarks

Major IRL studies span feature selection, robotic manipulation, multi-modal HRI, and grid-based robot navigation:

Feature Selection: Eight real-world datasets (Pen-Digit, Forest Cover, etc.) have demonstrated that hybrid trainers and GCN+DTF architectures accelerate convergence versus vanilla RL by 30–50%, with up to +4 percentage points gain in Best Accuracy and +3 in Average Accuracy relative to baselines (Fan et al., 2020).
Robotic Learning with Trajectory Scoring: In standard sparse-reward Mujoco and MetaWorld domains, score-based IRL matched or exceeded preference-based methods’ performance, attaining near-optimal behaviors within 300–500 human trajectory scores—three to five times fewer queries than pairwise frameworks (Liu et al., 2023).
Warehouse Robot Navigation: In simulated 5×5 grid environments, IRL Q-learning reached goal states in an average of 16.1 steps (75% success rate) and converged in ∼50 episodes, outperforming SARSA under identical feedback provision (Bora, 16 Jul 2024).
Multi-modal HRI: Multi-modal IRL approaches combining speech and gesture yielded faster convergence and higher rewards than unimodal IRL or RL-only setups; affordance-driven filtering further improved both learning speed and final success rates (Cruz et al., 2018).

6. Practical Guidelines and Limitations

Empirical findings yield several principled guidelines for practitioners:

Off-policy learning algorithms are recommended for maximizing the value of sparse or asynchronous human feedback (Bora, 16 Jul 2024).
Fixed, symmetric feedback scales and vigilant operator assignment minimize normalization requirements and bias (Bora, 16 Jul 2024).
Feedback provision is most beneficial early in training, with tapering as policy confidence increases (via ε-decay) (Bora, 16 Jul 2024).
Combining multi-modal feedback with affordance-based pruning substantially increases robustness and accelerates learning (Cruz et al., 2018).
Score-based IRL should implement adaptive smoothing to guard against unreliable scoring, especially in domains with noisy or subjective evaluation (Liu et al., 2023).
Hyperparameter sensitivity and “warm-up” phases are bottlenecks in score-based IRL, necessitating careful tuning (Liu et al., 2023).

A plausible implication is that for large-scale or high-dimensional domains, integrating structured domain knowledge (e.g., feature hierarchies, affordances) into both the state representation and the reward signal is essential for scalable, data-efficient IRL.

IRL is methodologically distinct from traditional Inverse RL (also sometimes abbreviated “IRL”) in that the reward is not inferred from passive observation but actively shaped via dynamic trainer input. The design patterns of IRL connect to human-in-the-loop learning, value alignment in autonomous systems, data-efficient RL for robotics, and explainable/reliable ML. As real-world deployment grows, open questions remain regarding optimal human feedback scheduling, multi-trainer consistency, scaling to deep RL, and the theoretical limits of feedback efficiency. Adaptive mediation between learned and instructed behaviors, robustness under multi-modal and imperfect input, and reward model interpretability are identified as central ongoing challenges (Fan et al., 2020, Cruz et al., 2018, Liu et al., 2023, Bora, 16 Jul 2024).