reinforcement learning

Reinforcement learning reshapes cooperation in interaction dependent asymmetric games

Path planning for underwater gliders combining acoustic field propagation characteristics and deep reinforcement learning

An Explicit Interaction-Prompted Diffusion Framework for High-Fidelity 3D Molecular Generation.

Current structure-based drug design generative models often struggle to faithfully recapitulate genuine ligand-protein binding interactions. Instead, under the coupling of implicit learning architectures and biased training data, they tend to learn spurious statistical correlations. To address this, we propose EIP-Diff (Explicit Interaction-Prompted Diffusion), an architecture featuring a novel explicit interaction-prompt embedding mechanism that is better suited for real-world target-specific drug design. This architecture replaces biased implicit learning with explicit, residue-level biological guidance, thereby promoting more fine-grained geometric fidelity and more precise interaction-aware conditioning. To fully realize the capabilities of EIP-Diff and provide a reliable basis for performance evaluation, we further constructed CrystalData set, which provides higher-fidelity and less-biased structural supervision than existing data sets. This explicit architecture markedly improves distribution consistency: even when trained on the crossdocked data set, EIP-Diff achieves the highest alignment with authentic pharmacological distributions among evaluated models. Training on CrystalData set further enhances this alignment and improves 3D geometric accuracy, while retaining strong controllability, high chemical space coverage, and near-perfect uniqueness. In addition, target-based validation on KAT6A and YTHDC1 confirmed that EIP-Diff accurately recapitulates native-like binding modes. Furthermore, in a real-world drug design task against IDO1, we successfully designed a novel lead compound with nanomolar potency (IC50 = 0.31 nM). These results demonstrate that the EIP-Diff architecture can explicitly leverage experimentally derived structural data and biologically meaningful interaction information for target-specific molecular generation, thereby enabling its effective application to real-world structure-based drug design.

Robust reachability within deep reinforcement learning framework

Tri-driven graph reinforcement learning for dynamic root cause tracing in UF-RO water treatment processes

LLM powered adaptive traffic signal control via knowledge-enhanced prompt and reinforcement learning fine-tuning

Reinforcement learning-enhanced barrier function super-twisting sliding mode control for bidirectional electric vehicle charging with superconducting magnetic energy storage

LOGO: A framework for language-based goal decomposition and skill recomposition in reinforcement learning

Prescribed performance optimal control for autonomous aerial–ground heterogeneous systems via safe reinforcement learning

Multi-agent switching energy management strategy for fuel cell hybrid vehicles using an enhanced deep reinforcement learning algorithm

Seismic Control of a Smart Base-Isolated Building with Nonlinear Behavior Using Deep Reinforcement Learning

DRL is highlighted as a promising data-driven strategy for robust and adaptive control of nonlinear structural systems under partial observability by addressing a critical limitation of passive systems and accelerates the decay of residual vibrations.

Dynamics of Boltzmannian temporal difference reinforcement learning in social dilemmas