Semantic-driven Reinforcement Learning

• Semantic-driven Reinforcement Learning (SRL) is a framework that integrates semantic state abstractions, reward functions, and action selection to guide policy learning.
• SRL leverages explicit semantic cues like object-centric tuples, semantic maps, and label vectors to optimize performance in tasks such as transfer learning, bitrate control, and medical report generation.
• SRL methods yield interpretable, domain-adaptable policies with transparent decision processes, achieving robust zero-shot transfer and enhanced sample efficiency across diverse applications.

Semantic-driven Reinforcement Learning (SRL) designates a class of reinforcement learning frameworks in which semantic representations, semantic metrics, or semantic objectives directly steer policy learning, decision-making, or reward assignment. Unlike conventional RL approaches driven by raw sensory inputs or monolithic state signals, SRL methods leverage explicit semantic information—ranging from symbolic abstractions and pixelwise semantic maps to clinically grounded label vectors—to define state, action, or reward functions. This paradigm has demonstrated marked advantages in transfer learning, interpretability, adaptive bitrate-control, clinically correct language generation, and robust autonomous navigation across diverse application domains (Garcez et al., 2018, Li et al., 2021, Wang et al., 18 Dec 2025, Malczyk et al., 20 May 2025).

1. Foundational Principles of Semantic-guided Reinforcement Learning

SRL is characterized by the explicit encoding and use of semantic knowledge at critical points in the RL loop:

• Semantic State Abstractions: Constructs such as object-centric tuples ⟨type, x, y⟩ (Garcez et al., 2018), pixelwise semantic-importance maps (Li et al., 2021), radiological label-vectors (Wang et al., 18 Dec 2025), and segmentation masks (Malczyk et al., 20 May 2025) are used to abstract raw sensory input into domain-relevant, interpretable sub-states.
• Semantic Reward Functions: Rewards are shaped to reflect semantic correctness or task goals, e.g., MCCS (margin-based cosine similarity) over label embeddings (Wang et al., 18 Dec 2025), rate-distortion metrics weighted by semantic region importance (Li et al., 2021), collision-free inspection coverage of semantic objects (Malczyk et al., 20 May 2025), and causal reward attribution at interaction events (Garcez et al., 2018).
• Semantic-driven Action Selection: Policies in SRL aggregate action values by weighting semantic relations (e.g., proximity biases) or optimize semantic metric performance rather than sensory fidelity.

These principles differentiate SRL from classic RL, which typically lacks abstraction, domain knowledge, and semantic interpretability in policy or learning signals.

2. Representative SRL Frameworks and Mathematical Formulation

Distinct SRL frameworks implement semantic-guidance at different RL loop stages. Key methodologies include:

a. SRL+CS (Symbolic RL with Common Sense)

In (Garcez et al., 2018), input images are abstracted into sub-states representing relative positions between agent and objects, forming tuples $s^{k} = (\Delta x, \Delta y)$. SRL+CS maintains separate Q-tables $Q^{ij}(s^k,a)$ for each pair (agent/object type), allowing:

• Reward Attribution: Updates occur only for sub-states with direct agent-object contact $(s^k = (0,0))$ and non-zero reward.

$$Q^{ij}(s^k_t,a_t) \leftarrow Q^{ij}(s^k_t,a_t) + \alpha \left[r_{t+1} + \gamma \max_{a'} Q^{ij}(s^k_{t+1},a') - Q^{ij}(s^k_t,a_t)\right]$$

• Proximity-based Action Aggregation: Action selection is weighted by inverse square Euclidean distance:

$$a_{t+1} = \arg\max_{a \in \mathcal{A}} \sum_{i,j} \sum_k \frac{Q^{ij}(s^k_t, a)}{(d^k_t)^2}$$

b. Semantic Bit Allocation via Deep Q-learning

In (Li et al., 2021), bit allocation in HEVC is cast as an MDP, where the agent observes

$s_t = (L_t, M_{s,t}, g_t)$

and chooses a quantization parameter action $a_t$ from a discrete set. The reward combines local bitrate savings and semantic-region distortion:

$$r_{t+1} = \Delta\mathrm{Bpp}_t - \alpha_s \Delta M_{s,t}$$

c. Semantic RL for Medical Report Generation (MRG-R1)

In (Wang et al., 18 Dec 2025), clinical label alignment is formalized as a reward. Generated and reference reports are mapped to signed label vectors $z_j(y)$ and compared via margin-shaped cosine similarity (MCCS):

$$\mathrm{MCCS}(y,y^*;m) = \max\left( \frac{\mathrm{CCS}(y,y^*) - m}{1-m}, 0\right)$$

with group-relative advantages computed per batch, optimizing state-of-the-art clinical correctness under GRPO (Group Relative Policy Optimization).

d. Semantics-driven Inspection Path Planning

Semantic segmentation masks directly shape state ($\mathbf{D}_t(\mathbf{S}_t)$) and reward in inspection path planning (Malczyk et al., 20 May 2025), enforcing object-centric coverage and collision avoidance through inspection and discovery bonuses in a deep RL framework.

Framework	Semantic State	Semantic Reward
SRL+CS (Garcez et al., 2018)	Object-relational tuples	Causal reward (contact)
RSC (Li et al., 2021)	Semantic maps, global features	Bitrate-saving weighted by region importance
MRG-R1 (Wang et al., 18 Dec 2025)	Label vectors from CheXbert	MCCS (clinical label cosine)
Inspection RL (Malczyk et al., 20 May 2025)	Masked depth, occupancy grid, SVS map	Mesh coverage, semantic search, collision penalty

3. Integration of Semantic Knowledge in Learning Architectures

SRL architectures integrate semantic information at multiple levels:

• Input Modules: Direct feeding of object type/location tuples, semantic importance maps (Grad-CAM, Mask R-CNN), or binary segmentation masks.
• Network Branching: Local semantic inputs (e.g., $64 \times 64 \times 2$ tensors) are processed by dedicated convolutional branches; global features capture contextual cues (neighbor QPs, mask ratios) in video coding (Li et al., 2021).
• Output/Format Constraints: For LLMs, reasoning tags (> , <report>) reinforce structured semantic output (Wang et al., 18 Dec 2025), scored as part of the reward.

  • State Masking and Reward Coupling: In autonomous inspection, policy networks receive masked depth images, ensuring semantic features dominate network attention and reward calculation (Malczyk et al., 20 May 2025).

  The modular assignment of semantic features enhances specialization, generalization, and interpretability, allowing inspection of learned object-centric policies and controlled focus on critical regions.

  4. Empirical Performance and Transfer Characteristics

  SRL consistently exhibits superior learning efficiency, semantic accuracy, and transfer robustness compared to conventional RL methods:

  • SRL+CS Gridworld: Achieves zero-shot transfer (≈100% positive-collection accuracy) from deterministic training to random test layouts, whereas DSRL and DQN/DQN baselines yield at most 70% and 50% respectively (Garcez et al., 2018).

  • Semantic Bit Allocation (HEVC): Attains bitrate savings of 34.39%–52.62% under equivalent semantic fidelity for classification, detection, and segmentation tasks (Li et al., 2021).
  • Clinical Report Generation: MRG-R1 yields state-of-the-art CE-F1 scores (51.88, IU-XRay; 40.39, MIMIC-CXR) using label-semantic reward, outperforming token-supervised LLMs (Wang et al., 18 Dec 2025).
  • Inspection Path Planning: Robust sim2real transfer enables ~96% semantic-surface coverage in real-world scenes with automatic object switching and minimal crash rate (<1.5%) (Malczyk et al., 20 May 2025).

  Zero-shot or few-shot transfer learning emerges as a defining trait, enabled by SRL’s abstraction of local interaction rules that generalize across unseen spatial layouts or semantic configurations.

  5. Interpretability and Policy Transparency

  SRL approaches enhance interpretability in several respects:

  • Human-readable Policy Tensors: Tabular Q-tables $Q^{ij}(\Delta x, \Delta y)$ in SRL+CS are directly inspectable for analysis of action tendencies given object relations (Garcez et al., 2018).
  • Semantic Feature Attribution: DQN bit-allocation policies can be understood as fine-grained region prioritization reflecting downstream semantic utility (Li et al., 2021).
  • Structured Output in LLMs: Explicit reasoning and reporting sections aid auditability and error analysis in medical report generation (Wang et al., 18 Dec 2025).
  • Object-centric Coverage Maps: RL inspection agents’ focus and coverage of semantics is observable via face visitation heatmaps (Malczyk et al., 20 May 2025).

  This suggests SRL methods can yield transparent policies with semantic rationales, facilitating deployment in high-stakes domains and improving error diagnostics.

  6. Limitations, Future Directions, and Prospects

  Despite substantial advances, several limitations persist:

  • Generalization Gaps: Tabular or explicit object-pair Q-learning in SRL+CS does not extrapolate to unseen sub-states or reversed semantics (Garcez et al., 2018).
  • External Vision Modules: Symbolic extraction remains external; end-to-end learning of semantic representations is a prospective direction (Garcez et al., 2018, Li et al., 2021).
  • Metric Granularity: Clinical SRL frameworks such as MRG-R1 are limited by fixed label sets (CheXbert-14); richer entity–relation graphs and severity/saliency models may further improve semantic fidelity (Wang et al., 18 Dec 2025).
  • Computational Overheads: Semantic map generation and policy evaluation introduce runtime and memory costs (0.45s/frame in HEVC, 0.25s/frame QP decision), motivating acceleration and pipeline integration (Li et al., 2021).

  A plausible implication is that combining SRL with relational RL, planning, and model-based approaches, as suggested in (Garcez et al., 2018), could further enhance generalization and semantic adaptability. Advances in automatic semantic extractors and unified representation learning may unify SRL approaches across perceptual, control, and generative modeling tasks.

  7. Cross-domain Applications and Research Impact

  SRL frameworks have demonstrated efficacy across domains:

  • Symbolic RL for Abstract Reasoning and Generalization (Garcez et al., 2018)
  • Task-driven Semantic Coding for Efficient Media Transmission (Li et al., 2021)
  • Clinically Aligned Medical Report Generation (Wang et al., 18 Dec 2025)
  • Real-world Semantic Inspection Path Planning (Malczyk et al., 20 May 2025)

  The integration of semantic metrics into RL workflows enables task-aligned optimization, sample-efficient learning, and interpretable policy deployment. As research progresses, these mechanisms are poised to underpin semantic-aware agent design in diverse domains, ranging from autonomous robotics and media coding to clinical AI and structured language generation.