ROVER pretrains transferable exploration policies by maximizing occupancy coverage with a learned resolvent world model and virtual sink state, outperforming baselines on sparse navigation tasks.
Efficient exploration via state marginal matching
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
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.
SRCP improves zero-shot generalization of successor representation methods in visual unsupervised reinforcement learning via saliency-guided representations and consistency policies.
SENIOR improves feedback efficiency and policy learning speed in PbRL by combining motion-distinction query selection via kernel density estimation with preference-guided intrinsic rewards, showing gains on simulated and real robot tasks.
citing papers explorer
-
Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization
ROVER pretrains transferable exploration policies by maximizing occupancy coverage with a learned resolvent world model and virtual sink state, outperforming baselines on sparse navigation tasks.
-
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.
-
Saliency-Guided Representation with Consistency Policy Learning for Visual Unsupervised Reinforcement Learning
SRCP improves zero-shot generalization of successor representation methods in visual unsupervised reinforcement learning via saliency-guided representations and consistency policies.
-
SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
SENIOR improves feedback efficiency and policy learning speed in PbRL by combining motion-distinction query selection via kernel density estimation with preference-guided intrinsic rewards, showing gains on simulated and real robot tasks.