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

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Showing 2876–2900 of 15113 papers

TitleStatusHype
METRA: Scalable Unsupervised RL with Metric-Aware AbstractionCode1
Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments—0
Virtual Augmented Reality for Atari Reinforcement LearningCode0
Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learningCode0
Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy ApproachCode0
Novelty Detection in Reinforcement Learning with World Models—0
Discerning Temporal Difference Learning—0
A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real BuildingsCode0
Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration BiasCode1
Online RL in Linearly q^π-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore—0
Off-Policy Evaluation for Human Feedback—0
Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking—0
Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization—0
f-Policy Gradients: A General Framework for Goal Conditioned RL using f-Divergences—0
Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning—0
Scalable Semantic Non-Markovian Simulation Proxy for Reinforcement Learning—0
Bi-Level Offline Policy Optimization with Limited Exploration—0
Aligning Language Models with Human Preferences via a Bayesian ApproachCode1
Predictive auxiliary objectives in deep RL mimic learning in the brain—0
When is Agnostic Reinforcement Learning Statistically Tractable?—0
On Double Descent in Reinforcement Learning with LSTD and Random Features—0
Distributional Soft Actor-Critic with Three RefinementsCode2
Multi-timestep models for Model-based Reinforcement Learning—0
Safe Deep Policy AdaptationCode1
Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach—0
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Benchmark Results

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