Risk-aware active learning reduces generalization error.
problem Learning policies with minimal performance risk.
method Risk-aware active inverse reinforcement learning algorithm.
result Risk-aware active learning outperforms standard approaches.
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48 results for “Active Reinforcement Learning”
SAMBA improves safe reinforcement learning with active exploration metrics.
problem Safe reinforcement learning in dynamic systems.
method Combines probabilistic modelling, information theory, and statistics. Uses novel metrics for out-of-sample Gaussian process evaluation.
result Orders of magnitude reduction in samples and violations compared to state-of-the-art methods.
Paper proposes an active multi-step TD algorithm for reinforcement learning.
problem Challenging decision making and control tasks in reinforcement learning.
method Active stepsize learning and adaptive multi-step TD algorithm with context-aware mechanism.
result Competitive results compared to other reinforcement learning baselines on discrete and continuous space tasks.
Deep reinforcement learning learns optimal active learning policies.
problem Designing effective active learning algorithms for low annotation cost.
method Meta-learning approach using deep reinforcement learning to train an active query policy network.
result Trained active learners can generalize across diverse datasets.
ARL uses queries to learn rewards, focusing on cost vs. reward value.
problem How to efficiently use queries to learn rewards in reinforcement learning.
method Proposed and evaluated heuristic approaches for ARL in multi-armed bandits and MDPs.
result Challenging aspects of ARL highlighted, including intractability of value computation.
Active inference enhances RL by balancing exploration and exploitation.
problem Traditional RL's balance between exploration and exploitation is often suboptimal.
method Developed a new decision-making objective based on active inference.
result The new algorithm successfully balances exploration and exploitation on various RL benchmarks.
Active-GRPO improves molecular optimization by actively deciding when to imitate or self-improve.
problem Training robust and efficient molecular optimization models with large language models.
method Active-GRPO combines imitation and reinforcement learning, upgrading references and policies dynamically.
result Improves molecular optimization performance, achieving statistically significant gains.
WiGS improves active learning for regression by dynamically selecting informative samples.
problem Reducing labeling costs in regression tasks.
method Formulated as a reinforcement learning problem, WiGS adapts the exploration-investigation balance.
result WiGS outperforms static methods in accuracy and labeling efficiency, especially in irregular data density.
AceIRL learns expert reward from active exploration.
problem Learning reward function from expert demonstrations in unknown environments.
method Active exploration to infer reward function and identify good policy.
result First approach to active IRL with sample-complexity bounds that doesn't require a generative model.
New algorithm helps avoid traps in reinforcement learning.
problem Existing reinforcement learning bounds assume no traps, limiting applicability.
method Introduces DRL (delegative reinforcement learning) allowing occasional advisor input.
result Derives a new regret bound without assuming episodic or trap-free environments.
New RL method optimizes power and accuracy for activity recognition.
problem Balancing power consumption and accuracy in wearable devices for activity recognition.
method Reinforcement Learning with multiple feedback sources for feature selection.
result Achieved good trade-off between power consumption and accuracy.
AIF reformulated as convex MDP for adaptive behavior.
problem Adaptive behavior and policy optimization.
method Formulating AIF as convex MDP, deriving mirror descent algorithm.
result EFE minimization in AIF is equivalent to reward maximization in latent MDP, with epistemic component.
Deep active inference learns policies from sensory inputs.
problem Learning policies in partially observable domains.
method Optimizes expected free energy with a variational autoencoder.
result Comparable or better performance than deep Q-learning.
Active learning improves RS-IRL by querying expert demonstrations to uncover risk boundaries.
problem Efficient learning from expert demonstrations in risk-sensitive IRL.
method Probabilistic disturbance sampling scheme for active learning.
result Our approach accelerates RS-IRL convergence with lower variance and unbiased results.
VAN method optimizes learning tasks with unified methods.
problem Optimizing learning tasks in active and reinforcement learning.
method Variational Adaptive-Newton method that unifies optimization, inference, and evolution strategies.
result VAN performs well on various learning tasks.
ARLD uses active deep Q-learning to learn faster with expert demonstrations.
problem Efforts required to obtain enough expert demonstrations for RL training.
method ARLD framework with Active Deep Q-Network (ADQN) for active demonstration queries.
result ARLD learns faster and reaches super-expert performance with expert demonstrations.
Dual active learning improves RLHF by selecting optimal conversations and teachers.
problem Efficiently aligning LLMs with human preferences using RLHF from feedback.
method Offline RL for conversation and teacher selection, dual active reward learning, pessimistic RL.
result The proposed algorithm achieves minimal generalized variance and outperforms state-of-the-arts.
New approach discovers active learning strategies for various domains.
problem Finding efficient active learning strategies across different data types.
method Formalized annotation as a Markov decision process, designed universal state and action spaces, introduced a reward function, and used reinforcement learning to find optimal strategies.
result Learned strategies consistently outperform existing methods on multiple unrelated domains.
Active IRL selects optimal human demonstrations for learning AI preferences.
problem Costly human demonstrations in IRL for autonomous systems.
method Information-theoretic acquisition function for selecting informative human demonstrations.
result Efficiently reduces human effort in learning AI preferences.
Active learning framework for optimizing human preferences in reinforcement learning.
problem Selecting most informative feedback for training models of human preferences.
method Proposes an active learning framework to collect preferential feedback online or offline.
result Errors in DPO logit estimates diminish with more feedback.
A new MARL framework for community-based cooperation with transfer and active exploration.
problem Flexible coordination patterns in multi-agent systems with community structures.
method Community-based multi-agent reinforcement learning with transfer and active exploration.
result Provably convergent actor-critic algorithms for structured information sharing and transfer learning.
Memory augmented neural networks improve active learning for one-shot predictions.
problem Scarcity and cost of labeled training data in deep architectures.
method Memory augmented neural networks and Class Margin Sampling (CMS) for reinforcement learning.
result The proposed method outperforms existing baselines in label predictions and reduces label requests.
A3RL combines online and offline RL with active sampling to improve policy learning.
problem Combining online and offline RL for sample efficiency and robustness.
method A3RL uses a confidence-aware Active Advantage Aligned (A3) sampling strategy to prioritize data from both online and offline sources.
result A3RL outperforms competing online RL techniques that use offline data.
MAX uses ensemble models to efficiently explore novel situations in reinforcement learning.
problem Efficient exploration in reinforcement learning.
method Model-based active exploration using ensemble forward models and Bayesian disagreement.
result MAX is at least an order of magnitude more efficient than baselines in semi-random environments.
A new Universal Activation Function improves performance across various machine learning tasks.
problem Achieving near optimal performance in different machine learning tasks.
method Optimization algorithms evolve the UAF's parameters to match the optimal activation function for each task.
result The UAF converges to near optimal performance in classification, quantification, and reinforcement learning tasks.
ARL uses MCTS to explore rewards efficiently, outperforming standard RL methods.
problem Challenging exploration in ARL due to limited reward feedback.
method ARL algorithm using MCTS, based on Bayes-Adaptive MDPs.
result Near-optimal performance on small problems, superior to Q-learners on larger ones.
This paper addresses the problem of learning a task from demonstration. We adopt the framework of inverse reinforcement learning, where tasks are represented in the form of a reward function. Our contribution is a novel active learning algorithm that enables the learning agent to query the expert for more informative d…
Paper tackles active learning for GNNs, reducing annotation costs.
problem Efficiently label nodes on graphs to reduce GNN training costs.
method Formulates as a sequential decision process, trains GNN-based policy network with reinforcement learning.
result Trains a transferable active learning policy that generalizes across different domains.
Paper proposes efficient RLHF methods for LLMs using active queries.
problem Efficiently aligning LLMs with human preferences using RLHF.
method Formalizes RLHF as a dueling bandit problem, introduces APPO and ADPO algorithms.
result ADPO achieves similar performance to state-of-the-art methods with fewer queries.
Improved reinforcement learning for robotics with active uncertainty reduction.
problem Infeasibility of model-free reinforcement learning methods in robotics due to safety and time constraints.
method Active uncertainty reduction-based virtual environments with adaptive sampling for metric self-improvement.
result Better modeling capacity for complex system dynamics compared to established methods.
Paper proposes a reinforcement learning method for efficient information acquisition.
problem Solving the Active Information Acquisition problem for robotic systems.
method Reinforcement Learning approach to choose actions for acquiring information.
result Efficient policies for online execution and real-time control of robotic systems.
Active inference implemented for high-dimensional tasks shows efficient exploration and improved sample efficiency.
problem Achieving efficient exploration and learning in complex, uncertain environments.
method Active inference framework applied to high-dimensional tasks, with Bayesian evidence maximization.
result Order of magnitude increase in sample efficiency over model-free baselines.
AFA evaluates AI feature acquisition strategies in domains with high costs.
problem Evaluate AI feature acquisition strategies in domains with high costs.
method Apply missing data methods and offline reinforcement learning under NDE and NUC assumptions.
result Propose a novel semi-offline reinforcement learning framework with three new estimators.
ALINE optimizes Bayesian inference and data acquisition by strategically querying informative data.
problem Strategic acquisition of informative data for Bayesian inference in challenging tasks.
method Unified framework combining amortized Bayesian inference and active data acquisition using a transformer architecture trained via reinforcement learning.
result ALINE delivers both instant and accurate inference along with efficient selection of informative points.
A framework for cost-effective feature selection in classification.
problem Sequentially selecting features to maximize prediction performance at minimum cost.
method Formulated as a reinforcement learning problem, a joint learning framework for RL agent and classifier is introduced. Orderless LSTM-based set encoding is used for feature subsets.
result Outperforms all baselines in prediction performance and feature acquisition cost on synthetic and real datasets.
Sparse representations improve reinforcement learning control policies.
problem Sparse representations are underused in reinforcement learning control.
method Incremental learning with sparse representations from neural networks, using distributional regularizers.
result Sparse representations avoid catastrophic interference and provide stable values for reinforcement learning.
New RL agent learns sparse rewards efficiently.
problem Sparse-reward environments and computational expense.
method Active inference with novel free energy minimization.
result High sample efficiency and online operation.
New RL algorithm tackles adversarial RMAB with unknown transitions and bandit feedback.
problem Learning in episodic RMAB with unknown transition functions and adversarial rewards.
method Developed a novel RL algorithm with a biased reward estimator and an index policy.
result Achieved regret bound for adversarial RMAB.
A DRL-based strategy improves vehicle tracking accuracy while saving energy.
problem Enhancing vehicle tracking accuracy in WSNs without increasing energy consumption.
method Decentralized strategy with dynamic reinforcement learning to adjust sensing areas.
result Simulation results demonstrate superior performance of DRL-aided design.
Study improves feature acquisition for static settings in AFAPE.
problem Evaluate AFAPE performance in static feature settings.
method Derive and adapt IPW, DM, and DRL estimators for MAR and MNAR missingness.
result Improved data efficiency in synthetic and real-world experiments.
RPI combines imitation and reinforcement learning to improve policies efficiently.
problem High sample complexity in reinforcement learning.
method Active interleaving between imitation and reinforcement learning, using oracle queries for exploration.
result RPI outperforms existing methods across various domains.
Bayesian optimization improves policy search in reinforcement learning.
problem Finding optimal policies with high variance estimates from random samples.
method Develops an algorithm combining Bayesian optimization and policy gradients.
result Improves sample complexity and reduces variance in empirical evaluations.
Efficient algorithm for mobile health provides timely physical activity suggestions.
problem Inefficient reinforcement learning for mobile health settings.
method Contextual bandit algorithm with linear mixed effects model and hyper-parameter updating.
result Improves speed and accuracy by up to 99% and 56%.
Proposes active learning for meta-learning in graph node response prediction.
problem Difficulty in improving performance with meta-learning due to unbalanced observations.
method Combines graph convolutional neural networks and reinforcement learning for both prediction and node selection.
result Can predict responses and select nodes even for unseen response variables.
Improves active learning by dynamically selecting the best acquisition function.
problem Lack of a universally successful acquisition heuristic in active learning.
method Trains an acquisition function as a predictor using reinforcement feedback.
result Always invents a superior acquisition function or adapts to the best heuristic.
Enhances reinforcement learning from sparse data.
problem Limited data for offline reinforcement learning.
method Trajectory-based data augmentation.
result Improves reinforcement learning performance.
RP1 uses active learning to improve world model in fewest samples.
problem Improving sample efficiency in MBRL for continuous control tasks.
method RP1 views MBRL as active learning, using a hybrid objective function and principled termination.
result Statistically significant gains over existing approaches on continuous control tasks.
AE-LSVI identifies near-optimal policies in complex systems with minimal data.
problem Identifying near-optimal policies in complex, costly data acquisition systems.
method Combines optimism and pessimism for active exploration in a generative model setting.
result Proves near-optimal policy identification over entire state spaces with polynomial sample complexity.