New algorithm tackles non-stationary reinforcement learning with general function approximation.
problem Understanding non-stationary MDPs with function approximation.
method Dynamic Bellman Eluder (DBE) dimension for complexity, sliding window mechanism, confidence set design.
result Upper bound on dynamic regret for proposed SW-OPEA algorithm.
Paper establishes new lower bounds for MDPs with changing transition kernels.
problem Minimizing sample complexity and regret in non-stationary MDPs.
method Developed novel lower bounds and constructed hard MDPs.
result Proved Ω ( ( H 3 S A / ε 2 ) log ( 1 / δ ) ) Ω((H^3SA/ε^2)\log(1/δ)) Ω (( H 3 S A / ε 2 ) log ( 1/ δ )) sample complexity lower bound. MetaCURL tackles non-stationary MDPs with optimal dynamic regret.
problem Online learning in non-stationary Markov decision processes.
method MetaCURL uses a meta-algorithm with multiple black-box algorithms and a sleeping expert framework.
result Achieves optimal dynamic regret without prior knowledge of MDP changes.
DARLING tackles non-stationary RL with guarantees, improving dynamic regret.
problem Non-stationary reinforcement learning in unknown change points.
method Detection Augmented Reinforcement Learning (DARLING) for tabular and linear MDPs.
result DARLING matches minimax lower bounds in tabular and linear MDPs.
KeRNS tackles non-stationary reinforcement learning in metric spaces.
problem Non-stationary reinforcement learning in metric spaces.
method KeRNS uses time-dependent kernels to model non-stationary Markov Decision Processes (MDPs).
result KeRNS achieves a regret bound that scales with the covering dimension and total variation of the MDP.
Study non-stationary MDPs using worst-case RL, proposing RATS algorithm.
problem Robust zero-shot planning in non-stationary stochastic environments.
method Model-Based Reinforcement Learning, worst-case approach.
result RATS algorithm demonstrates benefits over reference methods.
PROPO tackles non-stationary MDPs with efficient policy optimization.
problem Non-stationary MDPs with varying reward and transition kernels.
method PROPO, a periodic restarted optimistic policy optimization algorithm with sliding-window-based policy evaluation and improvement.
result PROPO achieves near-optimal performance in non-stationary MDPs.
ARL algorithm reduces adversarial MDP to bandit problems for reliable policy learning.
problem Learning reliable policies in non-stationary, adversarial MDPs.
method Adversarial Reinforcement Learning (ARL) algorithm that converts MDP to a sequence of adversarial bandit problems.
result Achieves optimal regret bound of O ( S A T H 3 ) O(\sqrt{SATH^3}) O ( S A T H 3 ) . Optimistic algorithm reduces regret in non-stationary linear MDPs.
problem Efficient learning in non-stationary linear MDPs with evolving reward and transition.
method OPT-WLSVI, an optimistic model-free algorithm using exponential weights.
result Achieves a regret bound of O ~ ( d 5 / 4 H 2 Δ 1 / 4 K 3 / 4 ) \widetilde{\mathcal{O}}(d^{5/4}H^2 Δ^{1/4} K^{3/4}) O ( d 5/4 H 2 Δ 1/4 K 3/4 ) . NVMDP framework tackles non-stationary MDPs with varying discount rates.
problem Challenges in non-stationary environments and infinite-horizon formulations for reinforcement learning.
method Introduces NVMDP framework that accommodates non-stationarity and varying discount rates.
result NVMDPs provide a flexible mechanism to shape optimal policies without altering state or action spaces.
New algorithm limits regret in changing MDPs.
problem Reinforcement learning in MDPs with time-varying rewards and transitions.
method Proposed an algorithm with performance guarantees for non-stationary policies.
result First variational regret bound for general RL setting.
This paper refines the weighted strategy for non-stationary parametric bandits and MDPs, improving regret bounds.
problem Non-stationary environments with gradual drifting patterns.
method Refined analysis framework for the weighted strategy, leading to simpler and more efficient algorithms.
result Improved regret bounds for linear bandits, generalized linear bandits, and self-concordant bandits.
New approach turns optimal stationary RL into non-stationary RL without prior knowledge.
problem Optimal RL in non-stationary environments without prior knowledge of non-stationarity.
method Black-box reduction of optimal stationary RL algorithms to non-stationary RL.
result Achieves optimal dynamic regret bounds in various RL settings.
Improved algorithm detects changes in RL environments with non-stationary MDPs.
problem Learning in non-stationary reinforcement learning environments.
method R-BOCPD-UCRL2 algorithm for MDPs with multinomial state transitions.
result Near-optimal theoretical guarantees in terms of false-alarm rate and detection delay.
Prognosticator improves performance in non-stationary MDPs.
problem Real-world applications often violate the stationary assumption in reinforcement learning.
method A policy gradient algorithm that maximizes a forecast of future performance.
result Prognosticator outperforms online adaptation techniques in non-stationary MDPs.
New RL method tackles dynamic MDPs with evolving rewards and states.
problem Dynamic MDPs with evolving rewards and states.
method Sliding Window Upper-Confidence bound for Reinforcement Learning (SWUCRL2-CW) and Bandit-over-Reinforcement Learning (BORL).
result Achieves dynamic regret bound for non-stationary MDPs.
Develops RL algorithm for non-Markovian, non-stationary reward streams.
problem Maximizing rewards from non-Markovian, non-stationary reward streams.
method Uses causal DAG to construct Markov states, solves periodic MDP.
result Optimal state construction maximizes discounted rewards.
New algorithm reduces dynamic regret for MDPs with unknown transition and adversarial rewards.
problem Episodic linear mixture MDPs with unknown transition and adversarial rewards.
method Combines occupancy-measure-based global optimization and policy-based variance-aware value-targeted regression.
result Achieves near-optimal dynamic regret of O ~ ( d H 3 K + H K ( H + P ˉ K ) ) \widetilde{\mathcal{O}}(d \sqrt{H^3 K} + \sqrt{HK(H + \bar{P}_K)}) O ( d H 3 K + H K ( H + P ˉ K ) ) . This paper shows CEM is a special case of TTM, leading to new proofs and improved sample complexity bounds.
problem Improving sample complexity for reinforcement learning algorithms.
method Viewing CEM as an application of TTM, deriving new proofs and bounds.
result Improved sample complexity bounds for CEM under various conditions.
Develops algorithms for reinforcement learning in non-stationary MDPs with dynamic regret bounds.
problem Sequential decision-making under temporal drifts in Markov decision processes.
method Sliding Window Upper-Confidence bound for Reinforcement Learning (SWUCRL2-CW) and Bandit-over-Reinforcement Learning (BORL) algorithms.
result Achieves superior empirical performance and low dynamic regret bounds in non-stationary RL settings.
In this paper, we propose a novel reinforcement- learning algorithm consisting in a stochastic variance-reduced version of policy gradient for solving Markov Decision Processes (MDPs). Stochastic variance-reduced gradient (SVRG) methods have proven to be very successful in supervised learning. However, their adaptation…
New algorithms reduce dynamic regret in non-stationary RL environments.
problem Optimizing policies in environments that change over time.
method POWER and POWER++ algorithms for policy optimization with dynamic regret analysis.
result POWER++ improves dynamic regret by actively adapting to non-stationarity.
Dividend yields have been widely used in previous research to relate stock market valuations to cash flow fundamentals. However, this approach relies on the assumption that dividend yields are stationary. Due to the failure to reject the hypothesis of a unit root in the classical dividend-price ratio for the US stock m…
New algorithm reduces offline RL data requirements significantly.
problem Optimizing policies using only historical data in reinforcement learning.
method Off-Policy Double Variance Reduction (OPDVR) algorithm.
result OPDVR achieves optimal sample complexity with O ( H 2 / d m ε 2 ) O(H^2/d_mε^2) O ( H 2 / d m ε 2 ) episodes. Online learning algorithms are designed to perform in non-stationary environments, but generally there is no notion of a dynamic state to model constraints on current and future actions as a function of past actions. State-based models are common in stochastic control settings, but commonly used frameworks such as Mark…
New definition resolves ambiguity in non-stationary bandit classification.
problem Ambiguity in classifying non-stationary bandits using existing definitions.
method Introducing a formal definition that resolves ambiguity and provides a unified approach.
result Unified approach applicable to both Bayesian and frequentist formulations, resolves classification issues.
Adaptive reduction scheme approximates optimal policy in regularized MDPs.
problem Finding near optimal policy in regularized MDPs with biased solutions.
method Adaptive reduction of regularization parameter λ to approximate optimal policy.
result Iteration complexity reduced for obtaining ε-optimal policy.
We study Exo-MDPs to reduce sample complexity in reinforcement learning.
problem Reducing sample complexity in reinforcement learning for structured MDPs.
method Introducing Exo-MDPs and proving structural equivalence to linear mixture MDPs, establishing regret bounds.
result Proved O ( H 3 / 2 d K ) O(H^{3/2}d\sqrt{K}) O ( H 3/2 d K ) regret bound for Exo-MDPs, matching lower bounds. In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and multi-modal optimal policy distribution of a sparse MDP. The full mathematical analysis of the proposed sparse MDP is provided.We first analyz…
SmoothFBO tackles non-stationary functional bilevel optimization.
problem Current FBO methods are limited to static offline settings and perform poorly in online, non-stationary scenarios.
method SmoothFBO introduces a time-smoothed stochastic hypergradient estimator with a window parameter to handle non-stationarity.
result SmoothFBO achieves sublinear regret and outperforms existing methods in non-stationary hyperparameter optimization and model-based reinforcement learning.
Proposes a new algorithm for non-stationary bandits.
problem Non-stationary reward distributions in contextual bandits.
method Multiscale changepoint detection for adaptive learning.
result Regret bound analysis and superior performance in experiments.
Study online ranking in non-stationary environments with abrupt preference changes.
problem Online learning to rank in a non-stationary cascade model where user preferences change abruptly.
method Cascading non-stationary bandits, proposing CascadeDUCB and CascadeSWUCB algorithms.
result Upper and lower bounds on regret for cascading non-stationary bandits algorithms.
Deep RL solves combinatorial selection problems with large item spaces.
problem Solving MDPs with large state and action spaces, especially for combinatorial selection.
method Convert S-MDP to IS-MDP, use weight-shared Q-networks to manage state space explosion.
result Our approach effectively handles large item spaces and scales to diverse environments.
New method approximates POMDPs with PB-MDPs, providing error bounds and practical algorithms.
problem Difficulty in solving POMDPs with continuous or hybrid state and observation spaces.
method Bounding particle filtering error and adapting MDP algorithms to POMDPs.
result General theory and practical algorithms for POMDPs with no direct dependence on state and observation space sizes.
Transformers achieve near-optimal dynamic regret in non-stationary reinforcement learning.
problem Understanding and handling non-stationary environments in reinforcement learning.
method Demonstrated that transformers can achieve nearly optimal dynamic regret bounds in non-stationary settings.
result Transformers can approximate and learn strategies for non-stationary environments, matching or outperforming existing expert algorithms.
Study incentivizes exploration in non-stationary MAB with compensation.
problem Incentivized exploration for non-stationary stochastic bandits with biased feedback.
method Proposed algorithms for abruptly-changing and continuously-changing non-stationary environments.
result Achieves sublinear regret and compensation over time.
Proposes a method to forecast non-stationary time series.
problem Challenges of non-stationary conditional distributions in deep learning.
method Bayesian dynamic model + deep conditional distribution model.
result Adapts to non-stationary time series better than state-of-the-art solutions.
New definitions of ESP for quantum reservoir computing handle non-stationary systems.
problem Traditional ESP does not apply to non-stationary systems.
method Introduce two new categories of ESP: non-stationary ESP and subset/subspace ESP.
result Demonstrates correspondence between non-stationary ESP and QRC with NARMA tasks.
Study combines VICReg and TNC for better encoding of non-stationary seismic signals.
problem Ineffective self-supervised learning on non-stationary time series.
method Combines VICReg and Temporal Neighborhood Coding (TNC).
result Effective for self-supervised learning on non-stationary seismic signals.
We consider large-scale Markov decision processes (MDPs) with parameter uncertainty, under the robust MDP paradigm. Previous studies showed that robust MDPs, based on a minimax approach to handle uncertainty, can be solved using dynamic programming for small to medium sized problems. However, due to the "curse of dimen…
We propose non-stationary spectral kernels for Gaussian process regression. We propose to model the spectral density of a non-stationary kernel function as a mixture of input-dependent Gaussian process frequency density surfaces. We solve the generalised Fourier transform with such a model, and present a family of non-…
A new method for CMDP solving without compromising safety constraints.
problem Solving CMDP problems while adhering to safety constraints.
method Decomposition into reconnaissance and planning MDPs.
result Achieves safe policies for any safety constraint set.
Optimizes learning policies in MDPs with weakly communicating structure.
problem Learning optimal policies in weakly communicating MDPs with generative model.
method Span-based approach, reducing to discounted MDPs for analysis.
result First minimax optimal sample complexity bound for weakly communicating MDPs.
Algorithm adapts to non-stationary rewards without prior knowledge.
problem Optimizing decisions in non-stationary environments without prior knowledge of changes.
method Optimization-based algorithm that restarts when non-stationarity is detected.
result Achieves tighter dynamic regret bound and is nearly minimax optimal.
New method removes oracle and reduces memory usage for robust MDPs.
problem Applying robust MDPs in practice due to model estimation and oracle requirements.
method Transformed robust MDPs into an alternative form allowing stochastic gradient methods and model-free approach.
result Sample-efficient algorithm with lower storage requirement and no oracle.
New RL method learns to skip states in linearly q π q^π q π -realizable MDPs, simplifying to linear MDPs.
problem Online RL in episodic MDPs with linearly q π q^π q π -realizable action-values. method Derives a novel algorithm that learns to skip states and applies a linear MDP algorithm.
result First polynomial-sample-complexity online RL algorithm for linearly q π q^π q π -realizable MDPs. DeepAveragers solves offline RL by solving derived MDPs from static data.
problem Offline reinforcement learning with limited data.
method Solves derived non-parametric MDPs (DAC-MDPs) using deep representations and costs for under-represented parts.
result The approach can lower-bound performance and scale to complex offline RL problems.
Study optimal pricing and inventory control in dynamic settings with censored demand.
problem Optimal pricing and inventory control in dynamic settings with censored demand.
method Approximate optimal policy via high-order MDP, propose novel algorithms for solving Bellman equations.
result Established finite-sample regret bounds and demonstrated efficacy through numerical experiments.