Study on identifying the best decision with continuous, separable rewards in stochastic multi-armed bandit.
problem Identifying the best decision with continuous, separable rewards in stochastic multi-armed bandit.
method Proposed an adaptive learning algorithm for CPE-CS problem, analyzed sample complexity with new hardness measure.
result Upper and lower bounds of sample complexity for CPE-CS problem.
Decouples RL components for better task transfer and robustness.
problem Poor generalization in reinforcement learning to task domain changes.
method Separates learning into task representation, dynamics, and reward.
result Decoupling improves performance and robustness to domain changes.
HiDe learns hierarchical control for complex tasks by separating planning and control.
problem Solving long horizon control tasks with generalization to unseen scenarios.
method Functional decomposition of state-action spaces, RL-based planner, modular transfer of policy layers.
result Generalizes across unseen test environments and scales to longer horizons.
New algorithms reduce slate bandit regret for large slates, outperforming existing methods.
problem Non-separable reward functions in slate bandits with many slates.
method Design of algorithms with sub-linear regret.
result Sub-linear regret with respect to the time horizon for large number of slates.
A new method recovers rewards from behavior policies using classification and regression.
problem Recovering meaningful rewards from observed behavior in reinforcement learning.
method GenPQR, a modular procedure that estimates behavior policy, evaluates soft Q-function, and recovers normalized reward using classification and regression.
result GenPQR matches or improves reward recovery compared to DeepPQR, while being simpler and more modular.
This paper introduces a new reward shaping method for average-reward reinforcement learning.
problem Speeding up convergence to an optimal policy in average-reward reinforcement learning tasks.
method Developed a temporal logic-based approach to automatically generate reward shaping functions.
result The optimal policy can be recovered using the proposed reward shaping framework.
New algorithm for optimizing statistical utilities in bandits.
problem Optimizing statistical functionals of long-run reward distributions.
method Influence-function calculus for stochastic gradient estimation, entropic mirror-ascent algorithm.
result Regret bounds that separate optimization and estimation errors.
AIRL learns robust, generalizable reward functions from demonstrations.
problem Learning robust reward functions from demonstrations for changing environments.
method Adversarial Inverse Reinforcement Learning (AIRL) with hierarchical disentangled rewards.
result Generalizable policies and comparable results to state-of-the-art methods.
New algorithm for reward-free RL with linear function approximation, reducing sample complexity.
problem Efficiently learning optimal policies without prior reward information in complex environments.
method Developed an algorithm for reward-free RL in linear Markov decision processes, proving sample complexity bounds.
result Polynomial sample complexity in feature dimension and planning horizon, independent of states and actions.
WDAIL uses Wasserstein distance for more effective reward shaping in IL.
problem Fixed reward functions in GAIL limit performance on complex tasks.
method Introduces Wasserstein distance and PPO for improved reward shaping and stability.
result Significant performance improvement in complex MuJoCo tasks.
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 IRL algorithm for continuous state spaces with formal guarantees.
problem Finding a reward function for expert behavior in continuous state spaces.
method Modeling the system using orthonormal functions and providing correctness proofs.
result Proof of correctness and formal guarantees on sample and time complexity.
A new method optimizes in nonstationary environments with many arms efficiently.
problem Optimizing in nonstationary environments with a large number of arms.
method Gaussian interpolation to learn continuous Lipschitz reward functions in nonstationary environments.
result Efficiently learns continuous Lipschitz reward functions with O ∗ ( T ) \mathcal{O}^*(\sqrt{T}) O ∗ ( T ) cumulative regret. A new method for RL with continuous actions improves stability and scalability.
problem Stability and scalability issues in existing RL methods.
method Soft policy gradient with entropy regularization, combined with double sampling for soft Bellman equation.
result Outperforms off-policy prior methods in continuous action RL tasks.
Extends Hindsight Experience Replay to learn from multiple reward functions.
problem Learning policies for multiple reward functions without trial runs.
method Develops a method to use a reward function to calculate rewards for states not encountered, and learns policies for multiple reward functions.
result A single policy can generalize across all linear combinations of multi-objective rewards.
New framework for analyzing games with multi-dimensional singular controls and non-linear jumps.
problem Analyzing games with multi-dimensional singular controls and non-linear jump impacts.
method Probabilistic framework with novel class of MFGs (MFGs of parametrisations).
result Existence of equilibria and equivalence with MFGs of singular controls.
New model accounts for continuous human trajectories in robotics.
problem Inaccurate probabilistic models of human behavior in robotics.
method Developed a new probabilistic model that considers distances between continuous trajectories.
result The new model outperforms existing models in explaining human behavior and improving robot inference.
EGFs use ergodicity to simplify generative flows for easier training and imitation learning.
problem Challenges in training generative flows, especially in continuous settings and for imitation learning.
method EGFs leverage ergodicity to build simple flows with universality guarantees and tractable FM loss. They introduce a KL-weakFM loss for IL training without a separate reward model.
result EGFs simplify generative flow training and enable effective imitation learning.
A new approach for deep exploration in sparse reward reinforcement learning.
problem Slow or no learning in reinforcement learning with rare rewards.
method Long-term visitation count planning and decoupling exploration and exploitation.
result Significantly outperforms existing methods in sparse reward environments.
Paper analyzes online reinforcement learning with outcome-based feedback, providing efficient algorithms and fundamental limits.
problem Assigning credit to actions in reinforcement learning with only endpoint rewards.
method Develops a provably sample-efficient algorithm for online reinforcement learning with general function approximation.
result Achieves O ( C m c o v H 3 / ε 2 ) O(C_{
m cov} H^3/ε^2) O ( C m co v H 3 / ε 2 ) sample complexity, characterizing statistical separation between outcome-based and per-step rewards. Maximizes Rényi entropy for efficient exploration in reward-free RL.
problem Challenges of exploration in reward-free reinforcement learning.
method Maximizes Rényi entropy over state-action space in exploration phase; uses batch RL for planning phase.
result Effective and sample-efficient exploration leading to superior policies.
EXPO framework eliminates need for reward model, achieving better optimization.
problem Optimizing large language model responses without a separate reward model.
method Introduces EXPO framework that avoids reparameterization, directly optimizing preferences.
result Demonstrates better regularization and intuitive interpolation behaviors.
This paper presents a novel nonmyopic adaptive Gaussian process planning (GPP) framework endowed with a general class of Lipschitz continuous reward functions that can unify some active learning/sensing and Bayesian optimization criteria and offer practitioners some flexibility to specify their desired choices for defi…
Method explains MDP optimal policies and reward collection.
problem Understanding optimal policies and reward collection in MDPs.
method Deterministic MDPs with a method to map state space and explain rewards.
result Algorithm determines which rewards will be collected and their frequency.
Study reward-free RL in non-linear settings, improving efficiency and removing assumptions.
problem Improving sample efficiency in reward-free reinforcement learning for non-linear function approximation.
method Proposed RFOLIVE algorithm for minimal structural assumptions, analyzed hardness results for reward-free and reward-aware exploration.
result Statistical efficiency and hardness results under various structural assumptions, no need for reachability or explorability assumptions.
A model learns rewards from diverse demonstrations for structurally similar tasks.
problem Difficulty in learning reward functions from demonstrations in real-world applications.
method Deep latent variable model that infers rewards from heterogeneous demonstrations of related tasks.
result Model can infer rewards for new tasks from a single demonstration.
Inverse optimal control, also known as inverse reinforcement learning, is the problem of recovering an unknown reward function in a Markov decision process from expert demonstrations of the optimal policy. We introduce a probabilistic inverse optimal control algorithm that scales gracefully with task dimensionality, an…
This paper solves inverse reinforcement learning with options framework.
problem Reinforcement learning in complex environments with expert demonstrations.
method Gradient method for parametrized options, deducing Q-feature and reward feature spaces, optimal reward function selection.
result Recovered rewards provide effective solution to IRL problem and accelerate transfer learning.
Reward tweaking optimizes behavior for long-term goals by adjusting the reward function.
problem Optimizing behavior for long-term goals in reinforcement learning with unstable long planning horizons.
method Reward tweaking learns a surrogate reward function that induces optimal behavior for the original task.
result Reward tweaking guides agents towards better long-term returns while planning for short horizons.
We protect value function approximators in continuous state spaces from differential privacy attacks.
problem Protecting reward information from inverse reinforcement learning attacks in continuous state spaces.
method Adding functional noise to the value function iteratively to ensure differential privacy.
result Rigorous privacy guarantees and approximate optimality of the algorithm in discrete state spaces.
A new approach for specifying and synthesizing subroutines for optimizing metrics.
problem Specifying and optimizing subroutines for various metrics.
method Formalizing programming by rewards (PBR), using continuous-optimization techniques to synthesize decision functions as if-then-else programs.
result Synthesized decision functions are optimal in cases when rewards have nice properties.
Improves imitation learning in RL by learning reward function efficiently.
problem Lack of effective reward function approximation in AIRL for imitation tasks.
method Proposes Off-Policy AIRL that combines adversarial learning with efficient reward function approximation.
result Shows superior imitation performance and efficiency compared to state-of-the-art AIL algorithms.
Reward-free RL in linear MDPs is as hard as reward-aware RL.
problem Reward-free RL in linear MDPs without access to the reward function during exploration.
method Developed a computationally efficient algorithm with sample complexity O ~ ( d 2 H 5 / ε 2 ) \widetilde{\mathcal{O}}(d^2 H^5/ε^2) O ( d 2 H 5 / ε 2 ) . result Achieved optimal d d d dependence in linear MDPs for reward-free RL, matching the reward-aware RL setting. A new framework estimates expert policy support to create a reward function for imitation learning.
problem Imitation learning from expert trajectories without reinforcement signals.
method Estimating the support of the expert policy to compute a fixed reward function.
result Comparable or better performance than state-of-the-art methods on discrete and continuous domains.
Reward collapse occurs when ranking-based reward models yield uniform rewards for different prompts.
problem Reward collapse in aligning large language models with human preferences.
method Introduced a prompt-aware optimization scheme to derive closed-form expressions for reward distributions.
result Our prompt-aware utility functions significantly alleviate reward collapse during training.
Fine-tunes diffusion models to generate diverse samples with high genuine rewards.
problem Reward collapse in finetuning diffusion models.
method Entropy-regularized control against pretrained diffusion models.
result Efficient generation of diverse samples with high genuine rewards.
AutoRL automates reward tuning for RL, improving performance on complex tasks.
problem Suboptimal policies when using direct rewards in RL.
method Evolutionary search over RL agents to find optimal rewards.
result Improves RL performance on complex tasks.
Nonparametric Thompson Sampling achieves optimal regret for risk-averse bandits with sub-Gaussian rewards.
problem Optimizing risk-averse bandit problems with sub-Gaussian rewards.
method Anchor-free nonparametric Thompson Sampling algorithm ρ e x t − N P T S S G ρ ext{-}NPTS_{\mathrm{SG}} ρ e x t − N P T S SG . result Achieves regret matching the instance-dependent lower bound to leading order in log n \log n log n . SS-SARSA tackles recovering bandits by treating rounds as states.
problem Real-world rewards vary with the elapsed rounds since last pull.
method Proposes SS-SARSA, a new RL algorithm treating rounds as states.
result Achieves efficient learning with asymptotic convergence to optimal policy.
Proposes a new RL method to fine-tune flow-based models with arbitrary rewards.
problem Challenges in fine-tuning continuous flow-based generative models with arbitrary reward functions.
method Online Reward-Weighted Conditional Flow Matching with Wasserstein-2 Regularization (ORW-CFM-W2)
result Achieves optimal policy convergence with controllable trade-offs between reward maximization and diversity preservation.
PWIL learns agent behavior from expert using Wasserstein distance.
problem Learning agent behavior from expert demonstrations efficiently.
method Primal Wasserstein Imitation Learning (PWIL) with offline reward function.
result PWIL achieves expert behavior on MuJoCo tasks with minimal interactions.
ORIL learns a reward function from unlabeled data to improve robot learning.
problem Leveraging unlabeled data for robot learning.
method ORIL learns a reward function from demonstrator and unlabeled trajectories, annotates data, and trains an agent via offline reinforcement learning.
result ORIL consistently outperforms BC agents on various robotic tasks.
This paper interpolates reward functions to predict optimal value functions in MORL.
problem Finding optimal value functions in MORL requires recomputing for each set of weights.
method Interpolating reward function weights to smooth value function transformations.
result Smooth interpolation of optimal value functions over reward function weights.
We characterize the class of separable Banach spaces X X X such that for every continuous function f : X → R f:X\to\mathbb{R} f : X → R and for every continuous function ε : X → ( 0 , + ∞ ) ε:X\to\mathbb(0,+\infty) ε : X → ( 0 , + ∞ ) there exists a C 1 C^1 C 1 smooth function g : X → R g:X\to\mathbb{R} g : X → R for which ∣ f ( x ) − g ( x ) ∣ ≤ ε ( x ) |f(x)-g(x)|\leqε(x) ∣ f ( x ) − g ( x ) ∣ ≤ ε ( x ) and g ′ ( x ) ≠ 0 g'(x)\neq 0 g ′ ( x ) = 0 for all x ∈ X x\in X x ∈ X (that is, g g g has no…
Paper introduces a new time separation function for C 0 C^0 C 0 spacetimes.
problem Lower semicontinuity of time separation function for C 0 C^0 C 0 spacetimes. method Introduced nearly timelike curves to ensure lower semicontinuity.
result Lower semicontinuous time separation function for C 0 C^0 C 0 spacetimes. Enhances on-policy RL with past reward statistics and hot-wiring.
problem Challenges of sparse reward signals in on-policy methods.
method Multi-critic supervision and hot-wiring mechanism.
result Improves on-policy learning for sparse reward tasks.
Improved reinforcement learning with deep learning.
problem Extending MultiGrid Reinforcement Learning to work with deep learning.
method Combining potential-based reward shaping with a learned potential function from interaction, and adapting it for deep learning algorithms.
result DQN augmented with the approach performs significantly better on continuous control tasks.
A new method for reinforcement learning that adapts to different domains using auxiliary classifiers.
problem Training reinforcement learning agents to perform well in different domains with varying dynamics.
method Learning auxiliary classifiers to distinguish source-domain from target-domain transitions and modifying the reward function accordingly.
result The approach improves transfer performance in reinforcement learning tasks with varying dynamics.