SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8551–8600 of 15113 papers

TitleStatusHype
Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learning—0
PROGRESSOR: A Perceptually Guided Reward Estimator with Self-Supervised Online Refinement—0
Projected Natural Actor-Critic—0
Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning—0
Projected State-action Balancing Weights for Offline Reinforcement Learning—0
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data—0
Projection Implicit Q-Learning with Support Constraint for Offline Reinforcement Learning—0
Projective simulation applied to the grid-world and the mountain-car problem—0
Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help—0
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning—0
Collaboration Promotes Group Resilience in Multi-Agent AI—0
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method—0
Prompt-based Visual Alignment for Zero-shot Policy Transfer—0
Prompting Wireless Networks: Reinforced In-Context Learning for Power Control—0
Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding—0
Prompt-responsive Object Retrieval with Memory-augmented Student-Teacher Learning—0
Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization—0
Proofread: Fixes All Errors with One Tap—0
Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning—0
Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning—0
Prospective Artificial Intelligence Approaches for Active Cyber Defence—0
Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning—0
Prototyping three key properties of specific curiosity in computational reinforcement learning—0
Provable Benefit of Multitask Representation Learning in Reinforcement Learning—0
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning—0
What can online reinforcement learning with function approximation benefit from general coverage conditions?—0
Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes—0
Provable Fictitious Play for General Mean-Field Games—0
Provable General Function Class Representation Learning in Multitask Bandits and MDPs—0
Provable Hierarchy-Based Meta-Reinforcement Learning—0
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature—0
Provable Multi-Objective Reinforcement Learning with Generative Models—0
Provable Partially Observable Reinforcement Learning with Privileged Information—0
Provable Reinforcement Learning with a Short-Term Memory—0
Provable Reset-free Reinforcement Learning by No-Regret Reduction—0
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States—0
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation—0
Provable RL with Exogenous Distractors via Multistep Inverse Dynamics—0
Provable Self-Play Algorithms for Competitive Reinforcement Learning—0
Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea—0
Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces—0
Provably adaptive reinforcement learning in metric spaces—0
Provably Correct Automata Embeddings for Optimal Automata-Conditioned Reinforcement Learning—0
Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning—0
Provably Efficient Algorithm for Nonstationary Low-Rank MDPs—0
Provably Efficient Algorithms for Multi-Objective Competitive RL—0
Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning—0
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization—0
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data—0
Provably Efficient Convergence of Primal-Dual Actor-Critic with Nonlinear Function Approximation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified