A quality-aware exploration method using return-conditioned sigmoid scheduling and per-agent RSQ metrics achieves top-tier returns on seven cooperative MARL benchmarks.
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Exploration by random network distillation
12 Pith papers cite this work. Polarity classification is still indexing.
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TeLAPA maintains archives of behaviorally diverse yet competent policies aligned in a shared latent space to preserve plasticity and enable faster recovery after interference in continual reinforcement learning.
OpenAI Five achieved superhuman performance in Dota 2 by defeating the world champions using scaled self-play reinforcement learning.
QOED selects identifiable parameter directions via Fisher matrix eigenspace analysis and modifies exploration objectives to approximate ideal information gain under bounded nuisance assumptions, yielding 21-35% performance gains in robotic tasks.
SBC generates virtual environments via state blocking to expose agents to diverse suboptimal partner policies, yielding superior zero-shot coordination performance including with humans.
NEO induces compositional latent programs as world theories from observations and executes them to enable explanation-driven generalization.
QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
An actor-critic RL algorithm for low-rank MDPs achieves improved sample efficiency using solely a policy evaluation oracle.
Distill-Belief distills Bayesian information-gain signals from a particle-filter teacher into a compact student policy for fast closed-loop source localization and parameter estimation while avoiding reward hacking.
Extracting task vectors from the offline dataset for policy training improves zero-shot offline RL performance by an average of 20% over random sampling baselines.
A neuron-astrocyte network with dual-timescale memory reduces median path lengths up to sixfold in partially observable grid-world navigation tasks.
A PPO-trained transformer policy sparsifies dynamic graphs during RRT frontier exploration, cutting size by up to 96% and yielding the most consistent exploration rates across environments.
citing papers explorer
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Quality-Aware Exploration Budget Allocation for Cooperative Multi-Agent Reinforcement Learning
A quality-aware exploration method using return-conditioned sigmoid scheduling and per-agent RSQ metrics achieves top-tier returns on seven cooperative MARL benchmarks.
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Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning
TeLAPA maintains archives of behaviorally diverse yet competent policies aligned in a shared latent space to preserve plasticity and enable faster recovery after interference in continual reinforcement learning.
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Dota 2 with Large Scale Deep Reinforcement Learning
OpenAI Five achieved superhuman performance in Dota 2 by defeating the world champions using scaled self-play reinforcement learning.
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Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration
QOED selects identifiable parameter directions via Fisher matrix eigenspace analysis and modifies exploration objectives to approximate ideal information gain under bounded nuisance assumptions, yielding 21-35% performance gains in robotic tasks.
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Shaping Zero-Shot Coordination via State Blocking
SBC generates virtual environments via state blocking to expose agents to diverse suboptimal partner policies, yielding superior zero-shot coordination performance including with humans.
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Learning to Theorize the World from Observation
NEO induces compositional latent programs as world theories from observations and executes them to enable explanation-driven generalization.
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QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
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Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs
An actor-critic RL algorithm for low-rank MDPs achieves improved sample efficiency using solely a policy evaluation oracle.
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Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields
Distill-Belief distills Bayesian information-gain signals from a particle-filter teacher into a compact student policy for fast closed-loop source localization and parameter estimation while avoiding reward hacking.
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Improving Zero-Shot Offline RL via Behavioral Task Sampling
Extracting task vectors from the offline dataset for policy training improves zero-shot offline RL performance by an average of 20% over random sampling baselines.
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Dual-Timescale Memory in a Spiking Neuron-Astrocyte Network for Efficient Navigation
A neuron-astrocyte network with dual-timescale memory reduces median path lengths up to sixfold in partially observable grid-world navigation tasks.
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Learning-Based Sparsification of Dynamic Graphs in Robotic Exploration Algorithms
A PPO-trained transformer policy sparsifies dynamic graphs during RRT frontier exploration, cutting size by up to 96% and yielding the most consistent exploration rates across environments.