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 8551–8575 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
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Benchmark Results

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