Random exploration optimizes Bayesian optimization with optimal error rates and computational efficiency.
problem Optimizing Gaussian Process models in Bayesian optimization.
method Random sampling from a distribution in an infinite dimensional Hilbert space, with domain shrinking and order-optimal regret guarantees.
result Achieves optimal error rates and computational efficiency in both noise-free and noisy settings.
The Exploration-Exploitation tradeoff arises in Reinforcement Learning when one cannot tell if a policy is optimal. Then, there is a constant need to explore new actions instead of exploiting past experience. In practice, it is common to resolve the tradeoff by using a fixed exploration mechanism, such as ε ε ε -greedy ex…
A single policy suffices for near-optimal parallel exploration in RL.
problem Quantitative effects of parallel exploration in reward-free RL.
method Using a single policy to guide exploration across all agents.
result Near-linear speedup and near-minimax optimality for linear MDPs.
The paper analyzes the intrinsic exploration terms in policy-gradient algorithms.
problem Exploration in policy-gradient algorithms and its impact on policy optimization.
method Numerical optimization criteria and stochastic gradient analysis.
result Exploration techniques improve policy optimization by smoothing the learning objective and modifying gradient estimates.
New L 1 L_1 L 1 -Coverage objective simplifies exploration in reinforcement learning.
problem Challenges in exploration for high-dimensional domains.
method Introduces L 1 L_1 L 1 -Coverage objective to enable efficient exploration and planning. result First computationally efficient algorithms for online reinforcement learning with low coverability.
Optimal control in latent factor models uses Tsallis entropy for exploration.
problem Optimal control in models with latent factors.
method Reward exploration with Tsallis entropy and derive q q q -Gaussian distribution over states. result Optimal policy derived in a model-agnostic setting.
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…
MPPO improves exploration efficiency for on-policy methods.
problem Inefficient exploration in on-policy methods.
method MPPO uses a population of diverse policies to enable better exploration.
result MPPO significantly outperforms state-of-the-art exploration methods.
Optimizes pure exploration in linear bandits with a new algorithm.
problem Best-arm identification in linear stochastic bandits.
method Developed the first asymptotically optimal algorithm for fixed-confidence pure exploration in linear bandits.
result Avoids the pitfall of a simple but difficult instance and bypasses the need to solve an optimal design problem.
An agent explores indefinitely in an environment with unlimited rewards.
problem Balancing exploration and exploitation in environments with unlimited rewards.
method Simple example of an environment with unbounded rewards and optimal agent behavior.
result An optimal agent always explores to maximize rewards, regardless of accumulated knowledge.
The contextual bandit literature has traditionally focused on algorithms that address the exploration-exploitation tradeoff. In particular, greedy algorithms that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy algorithms are desirable in practical setti…
Study uses RL to optimize investment with financial constraints, showing exploration benefits.
problem Optimal investment with financial constraints in continuous time.
method Reinforcement learning framework, focusing on Gaussian and truncated Gaussian distributions.
result Exploration leads to more dispersed wealth distribution with heavier tails, especially with smaller exploration parameters.
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.
Bayesian optimization offers the possibility of optimizing black-box operations not accessible through traditional techniques. The success of Bayesian optimization methods such as Expected Improvement (EI) are significantly affected by the degree of trade-off between exploration and exploitation. Too much exploration c…
LES reduces over-exploration in LSO, improving solution quality.
problem Over-exploration in LSO leads to unrealistic solutions.
method LES uses a decoder's approximation of the data distribution to mitigate over-exploration.
result LES enhances solution quality while maintaining high objective values.
DETC algorithm achieves asymptotic optimality in multi-armed bandit problems.
problem Multi-armed bandit problem with subgaussian rewards.
method DETC algorithm with two exploration and exploitation phases.
result DETC achieves asymptotically optimal regret bound.
Study task-guided exploration in linear dynamical systems, improving sample complexity.
problem Efficiently learning about an environment to complete a specific task.
method Proposed a computationally efficient experiment-design based exploration algorithm.
result Optimally explores the environment, collecting precise information needed to complete the task.
New findings on when to use action space exploration in reinforcement learning.
problem Understanding when to use action space exploration over traditional methods.
method Theoretical analysis and empirical testing of simple exploration methods.
result Exploration in action space is preferred when parametric complexity exceeds action space dimensionality and horizon length.
New algorithm explores optimally using same primitives as Thompson Sampling.
problem Optimal exploration of unknown parameter vectors with noisy measurements.
method Algorithm leveraging sampling and argmax oracles.
result Achieves exponential convergence rate, optimal among all allocations.
Algorithm achieves optimal pricing with minimal exploration for dynamic markets.
problem Optimal pricing in dynamic markets with contextual information.
method Localized exploration-then-commit (LetC) algorithm with pure exploration, refinement, and exploitation stages.
result Achieves minimax optimal, dimension-free regret bound.
New meta-RL method avoids exploration-exploitation trade-off.
problem Learning to explore and exploit simultaneously in meta-RL.
method Developed new objectives for exploration and exploitation.
result DREAM outperforms existing methods on complex tasks.
EQO uses a simple bonus term for efficient exploration in tabular RL.
problem Achieving minimax optimal reinforcement learning with practical efficiency.
method Exploration via Quasi-Optimism, employing a bonus proportional to state-action visit count.
result Achieves the sharpest known regret bound for tabular RL.
In this article we explore an alternative approach to address deep exploration and we introduce the ISL algorithm, which is efficient at performing deep exploration. Similarly to maximum entropy RL, we derive the algorithm by augmenting the traditional RL objective with a novel regularization term. A distinctive featur…
Optimizes exploration for nonlinear systems to learn controllers efficiently.
problem Learning optimal controllers for unknown nonlinear systems.
method Formally quantifies which parameters are most critical, and develops an algorithm to efficiently explore these parameters.
result Proves a near-instance-optimal rate for learning controllers.
Black-box optimizers that explore in parameter space have often been shown to outperform more sophisticated action space exploration methods developed specifically for the reinforcement learning problem. We examine these black-box methods closely to identify situations in which they are worse than action space explorat…
Intrinsically motivated reinforcement learning aims to address the exploration challenge for sparse-reward tasks. However, the study of exploration methods in transition-dependent multi-agent settings is largely absent from the literature. We aim to take a step towards solving this problem. We present two exploration m…
Paper presents an algorithm for optimal regret in communicating Markov decision processes.
problem Achieving optimal regret in Markov decision processes with a communicating assumption.
method The algorithm explicitly tracks the constant K(M) to learn optimally, balancing exploration, co-exploration, and exploitation.
result The algorithm achieves asymptotically optimal regret K ( M ) log ( T ) + o ( log ( T ) ) K(M) \log(T) + \mathrm{o}(\log(T)) K ( M ) log ( T ) + o ( log ( T )) for communicating Markov decision processes. Develops first optimal algorithm for logistic bandits.
problem Pure exploration in logistic bandits.
method Logistic track-and-stop (Log-TS) algorithm.
result Asymptotically matches lower bound for expected sample complexity.
OPPO optimizes policy learning with provable efficiency.
problem Designing efficient policy optimization algorithms that incorporate exploration.
method Proposes an Optimistic Proximal Policy Optimization (OPPO) algorithm.
result Achieves i l d e O ( d 2 H 3 T ) ilde{O}(\sqrt{d^2 H^3 T} ) i l d e O ( d 2 H 3 T ) regret in episodic MDPs. Optimally explores dynamical systems with varying properties using context inference.
problem Learning dynamics models for systems with varying properties.
method Formulates dynamics models as stochastic processes conditioned on a latent context variable inferred from system transitions. Uses probabilistic formulation to compute optimal action sequences for exploration.
result Demonstrates effectiveness of the method on non-linear toy-problems and reinforcement learning environments.
This research enhances exploration in DDPG using latent trajectory optimization.
problem Limited exploration in DDPG with deterministic policies.
method Model-based trajectory optimization for exploration in DDPG, using a learned deep dynamics model.
result Improved performance in continuous control tasks, especially with sparse rewards and images.
Bayesian optimization (BO) and its batch extensions are successful for optimizing expensive black-box functions. However, these traditional BO approaches are not yet ideal for optimizing less expensive functions when the computational cost of BO can dominate the cost of evaluating the blackbox function. Examples of the…
Randomized exploration in linear bandits achieves optimal regret bounds.
problem Optimizing exploration in high-dimensional linear bandit problems.
method Analysis of Thompson sampling without forced optimism.
result Randomized exploration algorithms achieve an O ( d n log ( n ) ) O(d\sqrt{n} \log(n)) O ( d n log ( n )) regret bound in smooth, strongly convex action spaces. 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.
A new multi-objective RL framework improves intrinsic exploration performance.
problem Sub-optimal exploration performance due to ad-hoc handling of intrinsic exploration.
method A multi-objective RL framework where both exploration and exploitation are optimized as separate objectives.
result EMU-Q method outperforms classic and other intrinsic RL methods on benchmarks.
New methods for efficient exploration under unknown linear constraints in bandits.
problem Optimizing decisions under unknown linear constraints in bandit problems.
method Lagrangian relaxation, computationally efficient extensions of existing methods, constraint-adaptive stopping rule.
result LAGEX achieves asymptotically optimal sample complexity, LATS shows asymptotic optimality up to novel constants.
This paper analyzes MORL and proposes efficient algorithms to learn Pareto optimal policies.
problem Understanding and efficiently learning Pareto optimal policies in multi-objective reinforcement learning.
method Systematic analysis of optimization targets, reformulation of Tchebycheff scalarization, online UCB-based algorithm, preference-free framework.
result Identification of Tchebycheff scalarization as a favorable method and efficient algorithms for learning Pareto optimal policies.
This work introduces CAET, an algorithm for cost-aware pairwise pure exploration.
problem Identifying optimal arm pairs with varying costs in multi-armed bandits.
method Introduces a framework for pairwise pure exploration with arm-specific costs, derives a lower bound, and proposes CAET algorithm.
result CAET optimizes cumulative cost and approaches the lower bound asymptotically.
BOKE optimizes expensive functions with reduced computational costs.
problem High computational cost of Gaussian process-based Bayesian optimization.
method Kernel regression and density-based exploration integrated into confidence bounds.
result BOKE achieves global convergence and superior computational efficiency.
Paper develops a dynamic Bayesian approach for active learning that optimizes exploration-exploitation balance.
problem Balancing exploration and exploitation in active learning for unknown functions.
method Develops BHEEM, a Bayesian hierarchical approach with approximate Bayesian computation for sampling trade-off parameters.
result BHEEM achieves at least 21% and 11% improvement over pure exploration and exploitation strategies respectively.
USAC balances pessimism and optimism in actor-critic training for better exploration and performance.
problem Excessive pessimism limits exploration, while excessive optimism leads to high-risk behaviors.
method Utility Soft Actor-Critic (USAC) dynamically adapts exploration based on critic uncertainty.
result USAC consistently outperforms state-of-the-art algorithms in continuous control tasks.
To maximize its success, an AGI typically needs to explore its initially unknown world. Is there an optimal way of doing so? Here we derive an affirmative answer for a broad class of environments.
Sample-efficient exploration is crucial not only for discovering rewarding experiences but also for adapting to environment changes in a task-agnostic fashion. A principled treatment of the problem of optimal input synthesis for system identification is provided within the framework of sequential Bayesian experimental …
New AIM algorithm optimizes exploration-exploitation in bandits.
problem Balancing exploration and exploitation in decision-making.
method Approximate Information Maximization (AIM) algorithm.
result AIM outperforms Infomax and Thompson sampling with enhanced speed and tractability.
New algorithm finds optimal policies without knowing reward functions.
problem Reward-agnostic exploration in reinforcement learning.
method Designs an algorithm that explores without reward information, achieving minimax optimality.
result Achieves provable minimax optimality in finding optimal policies for multiple reward functions.
New algorithm learns optimal exploration parameters for contextual bandits.
problem Learning optimal exploration in contextual bandits.
method Proposes two algorithms that learn optimal exploration parameters online based on context and reward.
result Demonstrates improved performance in learning optimal exploration compared to traditional methods.
ENIAC method optimizes and explores complex RL problems with non-linear policies.
problem Theoretical understanding of non-linear policies in RL with strategic exploration.
method ENIAC, an actor-critic method for non-linear function approximation.
result ENIAC finds near-optimal policies in polynomial exploration rounds under bounded eluder dimension.
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…