Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples. Developing efficient exploration strategies capable of finding the most useful samples becomes critical in such settings. Existing approaches towards finding effic…
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48 results for “adaptive exploration”
Transformer learns to infer partial MDPs for efficient in-context adaptation and exploration.
problem Efficiently adapt and explore in-context without gradient-based updates.
method Uses a transformer to learn inference from training tasks, considering hypothesis space of partial models.
result Adaptation speed and exploration-exploitation balance approach those of an exact posterior sampling oracle.
Adaptive exploration scheme for evaluating multiple policies with different rewards.
problem Online multi-reward multi-policy evaluation.
method Adapted -PAC perspective and MR-NaS exploration scheme to minimize sample complexity.
result Demonstrated effectiveness of adaptive exploration in tabular domains.
Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Frameworkmath.OC
Paper optimizes industrial refrigeration using adaptive exploration.
problem Challenges in optimizing real-time industrial processes with unknown characteristics and safety constraints.
method Adaptive and explorative real-time optimization framework with Gaussian process uncertainty quantification.
result Approach increases energy efficiency of refrigeration process, approximating complete information solutions.
Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.
problem Identifying outliers in a set of rewards where the threshold is a function of all rewards.
method Adaptively updated confidence interval for the threshold based on previous rounds' estimates, balancing exploration of individual arms and the outlier threshold.
result Efficient algorithm with reduced sample complexity for outlier detection.
Paper proposes Adaptive Pareto Exploration for identifying Pareto optimal arms in multi-objective scenarios.
problem Identifying Pareto optimal arms in multi-objective scenarios with relaxed constraints.
method Adaptive Pareto Exploration strategy for different relaxations of Pareto Set Identification.
result Reduction in sample complexity when identifying at most k Pareto optimal arms.
cKAM improves adaptive sampling by incorporating a cyclical stepsize scheme.
problem Adaptive Metropolis algorithms can get stuck in local modes.
method cKAM uses a cyclical stepsize scheme to encourage exploration and escape from local modes.
result cKAM successfully escapes local modes and converges to the true posterior distribution.
SPAQL improves RL by adaptively partitioning state-action space and learning a time-invariant policy.
problem Efficient model-free reinforcement learning with scalable algorithms.
method Adaptive Q-learning with UCB and Boltzmann exploration, automatically tuning temperature.
result SPAQL converges faster and uses fewer resources than AQL, showing higher sample efficiency.
Meta-SAGE improves deep RL scalability for CO tasks by adapting pre-trained models to larger-scale problems.
problem Improving scalability of deep reinforcement learning models for combinatorial optimization tasks.
method Meta-SAGE combines a scale meta-learner and scheduled adaptation with guided exploration to adjust model parameters for larger-scale problems.
result Meta-SAGE outperforms previous methods and significantly improves scalability in CO tasks.
Plan2Explore learns new tasks efficiently through self-supervised planning.
problem Challenges in reinforcement learning, especially task-specific learning and sample efficiency.
method Self-supervised exploration and fast adaptation to new tasks through efficient planning.
result Plan2Explore outperforms prior methods in learning new tasks without supervision.
We introduce the community exploration problem that has many real-world applications such as online advertising. In the problem, an explorer allocates limited budget to explore communities so as to maximize the number of members he could meet. We provide a systematic study of the community exploration problem, from off…
FLEX optimizes exploration for nonlinear systems with minimal data.
problem Efficient exploration of unknown nonlinear systems with limited data.
method FLEX uses optimal experimental design to maximize information gain.
result FLEX outperforms other methods in nonlinear environments and control tasks.
Proposes a method to avoid excessive exploration in reinforcement learning.
problem Avoiding excessive exploration in reinforcement learning to deploy it in practice.
method Designs a novel algorithm using UCB reinforcement learning policy with adaptive exploration constraints.
result Proves that the approach remains conservative while minimizing regret in tabular settings and validates on real-world tasks.
We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade-off between exploration and exploitation in this adaptation. Borrowing ideas from the bandits literature, we propose Daisee, a partition-based AIS algorithm. We further introduce a notion of regret for AI…
CARL safely adapts RL agents for safety-critical tasks.
problem Safety hazards in RL for safety-critical tasks.
method CARL combines model-based RL and cautious adaptation.
result CARL achieves higher rewards with fewer failures in safety-critical tasks.
ManifoldMind uses adaptive-curvature probabilistic spheres for trustworthy recommendations in semantic hierarchies.
problem Sparse and abstract recommendation domains where users explore diverse conceptual paths.
method Adaptive-curvature probabilistic spheres, soft multi-hop inference, and curvature-aware semantic kernel.
result Superior NDCG, calibration, and diversity compared to baselines on public benchmarks.
AER dynamically adjusts entropy regularization for better LLM reinforcement learning.
problem Policy entropy collapse in RLVR training limits exploration and reasoning performance.
method Adaptive Entropy Regularization (AER) with difficulty-aware coefficient allocation, initial-anchored target entropy, and dynamic global coefficient adjustment.
result AER consistently outperforms baselines on mathematical reasoning benchmarks, improving both accuracy and exploration.
We introduce inference trees (ITs), a new class of inference methods that build on ideas from Monte Carlo tree search to perform adaptive sampling in a manner that balances exploration with exploitation, ensures consistency, and alleviates pathologies in existing adaptive methods. ITs adaptively sample from hierarchica…
In this paper, we propose and study opportunistic contextual bandits - a special case of contextual bandits where the exploration cost varies under different environmental conditions, such as network load or return variation in recommendations. When the exploration cost is low, so is the actual regret of pulling a sub-…
We present the Variational Adaptive Newton (VAN) method which is a black-box optimization method especially suitable for explorative-learning tasks such as active learning and reinforcement learning. Similar to Bayesian methods, VAN estimates a distribution that can be used for exploration, but requires computations th…
Paper closes the gap in MP-MAB problems with novel adaptive communication and exploration.
problem Closing the gap between decentralized MP-MAB and natural centralized lower bound.
method BEACON: Batched Exploration with Adaptive COmmunicatioN, incorporating ADC and batched exploration.
result Proves logarithmic regret for a generalized MP-MAB problem.
Unified algorithm for efficient pure exploration using dual variables.
problem Efficiently achieving a specific goal through adaptive experimentation.
method Introducing dual variables to derive optimal allocation conditions, leading to Information-Directed Selection.
result Top-two Thompson sampling attains asymptotic optimality for Gaussian best-arm identification.
New method learns adaptive exploration strategies for dynamic tasks.
problem Learning effective exploration strategies in changing environments.
method Informed policy regularization to reduce sample complexity of RNN-based policies.
result Method learns efficient exploration strategies balancing information gathering and reward maximization.
This research tackles balancing exploration and exploitation in deep RL for partially observable systems.
problem Balancing exploration and exploitation in deep RL for partially observable systems.
method Deployed and tested several techniques including adaptive and deterministic exploration strategies, and a modified quadratic loss function.
result Adaptive methods better approximate the trade-off between exploration and exploitation.
New algorithm tackles high-dimensional contextual bandits without sparsity.
problem High-dimensional linear contextual bandit problem with large feature space.
method Proposes explore-then-commit (EtC) and adaptive explore-then-commit (AEtC) algorithms.
result Derives optimal rate for ETC algorithm and shows adaptive AEtC achieves it.
In this paper, we propose and study opportunistic bandits - a new variant of bandits where the regret of pulling a suboptimal arm varies under different environmental conditions, such as network load or produce price. When the load/price is low, so is the cost/regret of pulling a suboptimal arm (e.g., trying a suboptim…
Testing two potentially multivariate variables for statistical dependence on the basis finite samples is a fundamental statistical challenge. Here we explore a family of tests that adapt to the complexity of the relationship between the variables, promising robust power across scenarios. Building on the distance correl…
Paper explores BERT's efficiency on SQuAD2.0, freezing layers and using adapters.
problem Improving BERT's efficiency for SQuAD2.0 while maintaining performance.
method Freezing transformer layers, using adapters, and context-aware convolutional filters.
result Context-aware convolutional filters do not improve practical efficiency.
Optimizes biomolecular simulations by ranking adaptive sampling policies.
problem Efficiently sampling biomolecular systems to capture complex dynamical behaviors.
method Metric-driven ranking of adaptive sampling policies to identify the optimal policy for each round.
result Different adaptive sampling policies lead to faster convergence and improved sampling performance.
We show how an ensemble of -functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit setting, and adapt them to the -learning setting. We propose an exploration strategy based on upper-confidence bounds (UCB). Our experimen…
Unified framework for response-adaptive targeting in multi-treatment experiments
problem Improving ethical and statistical efficiency in multi-treatment clinical trials
method Response-adaptive targeting strategies
result Unified framework for -Rebalancing Targeting Strategies (RTS)
New approach for reward-free exploration reduces estimation error.
problem Reward-free exploration in reinforcement learning.
method Adaptive approach reducing MDP estimation error.
result Reward-free UCRL algorithm improves sample complexity.
New method improves smoothness of robot learning.
problem Jerky motion patterns on real robots from Deep RL exploration.
method Adapting state-dependent exploration to Deep RL algorithms with gSDE.
result Improved exploration and performance on real robots.
We propose the first fully-adaptive algorithm for pure exploration in linear bandits---the task to find the arm with the largest expected reward, which depends on an unknown parameter linearly. While existing methods partially or entirely fix sequences of arm selections before observing rewards, our method adaptively c…
Improved RL algorithm stabilizes unknown linear systems with polynomial regret.
problem Learning and stabilizing unknown linear dynamical systems.
method Proposes an algorithm with an improved exploration strategy for fast stabilization.
result Achieves regret after time steps.
Bayesian approach improves -greedy exploration in RL.
problem Improving -greedy exploration in model-free RL.
method Introducing a Bayesian model update for based on BMC.
result Proposed - exttt{BMC} algorithm efficiently balances exploration and exploitation.
Mortal bandits have proven to be extremely useful for providing news article recommendations, running automated online advertising campaigns, and for other applications where the set of available options changes over time. Previous work on this problem showed how to regulate exploration of new arms when they have recen…
We take initial steps in studying PAC-MDP algorithms with limited adaptivity, that is, algorithms that change its exploration policy as infrequently as possible during regret minimization. This is motivated by the difficulty of running fully adaptive algorithms in real-world applications (such as medical domains), and …
Paper simplifies complex AI exploration by predicting future rewards.
problem Training machines to optimally gather complex information.
method Developed a denser reward structure using cross-value to decouple exploration and exploitation.
result Demonstrated successful learning of challenging tasks without shaping or bonuses.
ASD algorithm maximizes model estimates by adaptively labeling points.
problem Maximizing model estimates through adaptive labeling of points in a sequential decision-making problem.
method Formulated a general information-directed sampling (IDS) algorithm with theoretical guarantees for linear, graph, and low-rank models.
result IDS algorithm outperforms in both simulation and real-data experiments for discovering chemical reaction conditions.
LNUCB-TA improves MAB performance by dynamically adjusting exploration rates and recognizing spatiotemporal patterns.
problem Suboptimal performance in environments with rapidly changing reward structures and static exploration rates.
method Hybrid model combining linear and nonlinear estimation, with adaptive k-NN for temporal attention.
result Significantly outperforms state-of-the-art algorithms in cumulative and mean reward, convergence, and robustness.
Adaptive teacher improves sample efficiency and mode coverage in sampling tasks.
problem Efficient exploration and mode coverage in sampling tasks with amortized inference.
method An adaptive training distribution (teacher) guides the training of the primary sampler (student).
result Improves sample efficiency and mode coverage across various tasks.
New sampling strategy improves TR algorithms for stochastic optimization.
problem Derivative-free stochastic optimization with Monte Carlo estimates.
method Stratified adaptive sampling to optimize MC sample size.
result Reduced sample complexity and superior efficiency confirmed.
New algorithm reduces regret by allowing free exploration in multi-armed bandits.
problem Designing an adaptive policy to minimize regret with a free exploration budget.
method Introduced -probably saving policies and a two-phase algorithm UFE-KLUCB-H.
result UFE-KLUCB-H accumulates strictly less regret than non-free exploration policies.
This paper explores how RL enhances HFT strategies in volatile markets.
problem Adapting to changing market dynamics in HFT.
method Deep Q-Learning applied to statistical arbitrage strategies.
result RL improves adaptability and profitability in HFT.
Proposes EE-Net for neural exploration in contextual bandits.
problem Exploitation-Exploration tradeoff in contextual bandits.
method Uses two neural networks: Exploitation and Exploration, to learn reward function and adaptively explore.
result Achieves regret and outperforms existing methods.
AgABC improves ABC algorithm by balancing exploration and exploitation.
problem Balancing global and local search abilities in ABC algorithm.
method Divide population into groups and assign different search strategies to members.
result Proposed AgABC algorithm outperforms other algorithms in accuracy and stability.
Study pure exploration in high-dimensional feature spaces using adaptive embeddings.
problem Overcoming the curse of dimensionality in pure exploration bandits.
method Adaptive embedding of feature representations into lower-dimensional spaces, carefully dealing with model misspecification.
result Sample complexity guarantees that depend on the effective dimension of feature spaces in kernel or neural representations.