New method reduces state distribution mismatch in off-policy RL.
problem State distribution mismatch in off-policy RL algorithms.
method Develops a novel constrained off-policy gradient objective to minimize state distribution shift.
result Minimizing state distribution shift improves performance in off-policy RL algorithms.
New method estimates state-action stationary distribution for better off-policy policy evaluation.
problem Accurately estimating state-action stationary distribution for off-policy policy evaluation.
method Estimated Mixture Policy (EMP) for state and state-action stationary distribution corrections.
result Empirical validation shows improved accuracy over state-of-the-art methods.
We study the problem of off-policy policy optimization in Markov decision processes, and develop a novel off-policy policy gradient method. Prior off-policy policy gradient approaches have generally ignored the mismatch between the distribution of states visited under the behavior policy used to collect data, and what …
Analyzes how economic policies affect wealth distribution in Bitcoin token economy.
problem Impact of economic policies on wealth distribution in token economies.
method Eliminated noise in wealth distribution data using macroeconomic and microeconomic time series. Causality analysis between BIPs and wealth distribution data.
result Proposed a structure for economic policy taxonomy in token economies.
A new method improves stability in policy learning for continuous control tasks.
problem Stability issues in policy gradient methods when policies are close to deterministic.
method Target Distribution Learning (TDL) alternates between proposing a target distribution and training the policy network to approach it.
result TDL leads to more stable policy improvements over iterations compared to existing methods.
A new method for continuous control avoids local movement issues.
problem Limitations of policy gradient methods in continuous control.
method Distributional framework and Generative Actor Critic (GAC) method.
result GAC outperforms policy gradient methods in continuous domains.
SDM Policy accelerates inference for robotic tasks while maintaining high action quality.
problem Prolonged inference times in diffusion-based policies for high-frequency control tasks.
method Two-stage optimization: score matching and distribution matching; dual-teacher mechanism.
result 6x inference speedup with state-of-the-art action quality.
This work learns exploration policies for unknown distributions using samples and policy gradients.
problem Learning exploration policies for unknown distributions in Bayesian bandits.
method Meta-learning approach parameterizing policies in a differentiable way and optimizing them with policy gradients.
result Effective gradient estimators and variance reduction techniques are derived.
DDO-RM improves reward-based policies by converting reward scores into a target distribution.
problem Improving reward-based policies when the reward function is simpler than the policy.
method Converts reward scores into a target distribution and uses KL-regularized mirror-descent updates.
result DDO-RM outperforms DPO in pair accuracy and mean margin.
New method learns policies without limiting to Gaussian distributions.
problem Limitations of Gaussian parameterization in policy learning.
method Advantage weighted quantile regression for implicit policy modeling.
result Comparable or superior performance on MuJoCo benchmarks.
New method uses entropy to improve policy gradient exploration.
problem Limited exploration in policy gradient methods.
method Entropy regularization with discounted future state distribution.
result Proves convergence to locally optimal policy.
In this paper, we propose a distributed off-policy actor critic method to solve multi-agent reinforcement learning problems. Specifically, we assume that all agents keep local estimates of the global optimal policy parameter and update their local value function estimates independently. Then, we introduce an additional…
Improves reinforcement learning policies for robustness.
problem Lack of robustness in reinforcement learning policies.
method Risk-aware Distributional Reinforcement Learning (SDPG) with CVaR.
result Risk-averse policies achieve robustness against disturbances.
Bayesian approach models policy distribution for faster exploration and transfer learning.
problem Challenges in exploration and adaptation to new tasks in transfer learning.
method Modeling a distribution over policies in a Bayesian deep reinforcement learning setup.
result Favorable experimental results on GridWorld and MiniGrid environments.
This work improves policy optimization by maximizing entropy of state distribution, leading to better exploration.
problem Lack of exploration in state space when maximizing policy entropy.
method Proposes maximizing the entropy of a lower bound approximation to the state weighting distribution, based on latent space representation.
result Entropy regularization based on marginal state distribution achieves superior state space coverage and better performance in various domains.
Paper learns domain randomization distributions for robust robot policies.
problem Finding good domain randomization parameters for simulation without real data.
method Gradient-based search methods to learn domain randomization distribution.
result Improvements in jump-start and asymptotic performance when transferring policies.
Off-policy learning exhibits greater instability when compared to on-policy learning in reinforcement learning (RL). The difference in probability distribution between the target policy (π) and the behavior policy (b) is a major cause of instability. High variance also originates from distributional mismatch. The var…
A new policy for contextual bandits adapts to reward vector shifts.
problem Learning under reward vector shifts with ordered rewards.
method Adaptive-discretization and optimistic elimination policy.
result Established upper bounds on preference-based regret.
The paper improves QD policy ensembles using distribution ratio estimators.
problem Training diverse and high-quality reinforcement learning agents.
method Using Stein variational gradient descent and distribution ratio estimators.
result The method generates diverse and high-quality reinforcement learning agents.
IDAC improves reinforcement learning efficiency by modeling implicit distributions.
problem Improving sample efficiency in reinforcement learning algorithms.
method IDAC uses two DGNs for a distributional critic and a semi-implicit actor to model implicit policy distributions.
result IDAC outperforms state-of-the-art algorithms on OpenAI Gym environments.
This work extends ME-RL using diffusion models to sample optimal policies.
problem Sampling from the optimal policy trajectory distribution in ME-RL.
method Introducing Diffusion-Augmented Markov Decision Processes (DA-MDPs) to minimize reverse KL divergence.
result DA-MDPs enable seamless integration into various ME-RL methods and outperform baselines.
Improved off-policy evaluation for MDPs with weak distributional overlap.
problem Evaluation of policies when target and data-collection distributions are not strongly overlapping.
method Truncated Doubly Robust (TDR) estimators for off-policy evaluation in MDPs under weak distributional overlap.
result TDR estimators can recover large-sample behavior and are consistent even when distribution ratios are not square-integrable.
The paper proposes a new policy for optimal treatment allocation based on quantile treatment effects.
problem Optimal treatment allocation policies that target distributional welfare, especially when individuals are heterogeneous.
method The approach involves allocating treatments based on the conditional quantile of individual treatment effects (QoTE), considering both prudent and negligent policymakers.
result The proposed minimax policies are robust to model uncertainty and can be generalized to various settings.
Unified framework for reliable uncertainty quantification in RL.
problem Uncertainty quantification in high-stakes reinforcement learning.
method Unified conformal prediction framework integrating distributional RL and conformal calibration.
result Significantly improved coverage and reliability over standard methods.
Wasserstein Policy Learning for Distributional Outcomes
problem Offline policy learning with distribution-valued outcomes
method Establishing statistical guarantees for policy learning framework
result Proven leading dependence on N and N-dim(Π) for finite-sample regret
New method for distributional off-policy evaluation using Bellman residual minimization.
problem Learning return distribution from offline data generated by a different policy.
method Energy Bellman Residual Minimizer (EBRM) method.
result Established finite-sample error bound for EBRM estimator.
Taylor expansions improve reinforcement learning policies.
problem Improving reinforcement learning policy optimization.
method Taylor expansion policy optimization.
result Taylor expansions enhance performance of distributed algorithms.
New method extends fitted Q-evaluation for distributional off-policy reinforcement learning.
problem Estimating return distribution in reinforcement learning using offline data.
method Developed a set of guiding principles and new FDE methods with theoretical justification.
result FDE methods outperform existing approaches in simulations and real-world games.
Paper proposes a policy-search algorithm to learn entropy-maximizing exploration policies in reward-free environments.
problem Reward-free learning in high-dimensional, continuous-control domains.
method Maximum Entropy POLicy optimization (MEPOL) algorithm that maximizes a non-parametric state entropy estimate.
result MEPOL learns a maximum-entropy exploration policy that facilitates learning various reward-based tasks.
CPME embeds counterfactual outcomes in RKHS for flexible policy evaluation.
problem Estimating counterfactual policy outcomes for decision-making.
method Counterfactual Policy Mean Embedding (CPME) framework in RKHS, plug-in and doubly robust estimators, kernel test statistic.
result Doubly robust estimator improves convergence rates and asymptotic normality.
Paper introduces DQPOPE for estimating return distributions in reinforcement learning.
problem Estimating the entire return distribution from off-policy data.
method Deep quantile process regression for distributional off-policy evaluation.
result DQPOPE achieves statistical advantages by estimating full return distribution with same sample size.
This paper tackles robust policy learning under concept drifts, improving upon existing methods.
problem Tackles robust policy learning under concept drifts, improving upon existing methods.
method Develops a doubly-robust estimator and a learning algorithm to maximize policy value within a given policy class.
result The proposed algorithm achieves sub-optimality gap of the order κ(Π)n−1/2, demonstrating substantial improvement over existing benchmarks. In this paper, we develop a multi-agent reinforcement learning (MARL) framework to obtain online power control policies for a large energy harvesting (EH) multiple access channel, when only causal information about the EH process and wireless channel is available. In the proposed framework, we model the online power co…
SDPG algorithm improves sample efficiency and reward in DRL for continuous action spaces.
problem Improving sample efficiency and reward in distributional reinforcement learning for continuous action spaces.
method SDPG algorithm models return distribution using samples via reparameterization technique.
result SDPG shows better sample efficiency and higher reward in OpenAI Gym environments.
This work uses MMD to find diverse policies in reinforcement learning.
problem Finding multiple near-optimal policies for a task.
method Formalizes policy difference as trajectory distribution discrepancy, uses MMD for optimization.
result Derives gradient-based optimization for diverse policy identification.
The paper tackles robust policy learning in MDPs using statistical methods.
problem Offline data-driven sequential decision making in MDPs.
method Evaluates policies using average rewards centered at policy-induced stationary distributions. Developed a statistically efficient method for estimating robust optimal policies.
result Established a rate-optimal regret bound up to a logarithmic factor.
This paper tackles distribution shift in model-based offline RL, proposing a shifts-aware reward method.
problem Distribution shift challenges model-based offline RL by distorting value estimation and policy optimization.
method The paper disentangles the problem into model bias and policy shift, proposing a shifts-aware reward through probabilistic inference.
result The proposed shifts-aware reward method effectively mitigates distribution shift and improves policy optimization.
Improves RL generalization by minimizing adversarial risk.
problem Overfitting to training environments and poor generalization to unseen scenarios.
method Introduces minimax formulation and distributional framework to RL.
result Trained policy shows improved generalization to different environments.
New algorithm stabilizes RL policy learning through divergence regularization.
problem Stabilize policy learning and improve performance in RL.
method Proximity term constraining discounted state-action visitation distributions to be close to each other.
result Proposed algorithm improves stability and final performance in RL tasks.
Improves off-policy evaluation weights for balanced state-action pair distribution.
problem Imbalance in importance sampling weights for off-policy evaluation of contextual bandits.
method Balanced Off-Policy Evaluation (B-OPE) method that minimizes imbalance to desired counterfactual distribution of state-action pairs.
result Experimental evidence shows B-OPE improves offline policy evaluation in both discrete and continuous action spaces.
RRPI improves offline RL by optimizing policies against worst-case dynamics.
problem Offline RL's performance degrades under distribution shift and transition uncertainty.
method Formulates offline RL as robust policy optimization, treating transition kernel as decision variable.
result RRPI achieves strong average performance on D4RL benchmarks, outperforming recent baselines.
DAC enhances exploration in reinforcement learning with entropy regularization.
problem Improving exploration efficiency in reinforcement learning.
method Sample-aware entropy regularization using replay buffer action distributions.
result DAC significantly outperforms existing algorithms in reinforcement learning tasks.
Paper addresses off-policy evaluation and learning with covariate shift.
problem Evaluating and training a new policy using historical data with a covariate shift.
method Derives efficiency bounds and proposes doubly robust estimators for OPE and OPL under covariate shift.
result Proposes estimators for off-policy evaluation and learning under covariate shift.
DualDICE estimates discounted distribution ratios for reinforcement learning datasets.
problem Accurate estimation of discounted stationary distribution ratios for reinforcement learning applications.
method Behavior-agnostic algorithm that avoids importance weights and is theoretically guaranteed.
result Significantly improves off-policy policy evaluation accuracy compared to existing techniques.
A nonparametric approach for policy learning for POMDPs is proposed. The approach represents distributions over the states, observations, and actions as embeddings in feature spaces, which are reproducing kernel Hilbert spaces. Distributions over states given the observations are obtained by applying the kernel Bayes' …
This work analyzes policy gradient methods in reinforcement learning, providing convergence and approximation guarantees.
problem Theoretical convergence and approximation error of policy gradient methods in reinforcement learning.
method Analysis of policy gradient methods in discounted MDPs, focusing on tabular and parametric policy classes.
result Provably characterizations of computational, approximation, and sample size properties of policy gradient methods.
New algorithm BEAR reduces instability in off-policy Q-learning.
problem High sensitivity of off-policy Q-learning methods to data distribution.
method Identified and mitigated bootstrapping error through constrained action selection.
result BEAR algorithm learns robustly from various off-policy distributions.
New algorithm bounds regret in mediator feedback bandit problems.
problem Mediator feedback bandit game with policy sets.
method Adopting EXP4 algorithm, new regret bounds based on policy set capacity.
result Nearly-matching lower bounds for policy set families.