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 1226–1250 of 15113 papers

TitleStatusHype
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations—0
Provable Partially Observable Reinforcement Learning with Privileged Information—0
Bilinear Convolution Decomposition for Causal RL Interpretability—0
BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings—0
o1-Coder: an o1 Replication for CodingCode3
RL-MILP Solver: A Reinforcement Learning Approach for Solving Mixed-Integer Linear Programs with Graph Neural Networks—0
HVAC-DPT: A Decision Pretrained Transformer for HVAC Control—0
Solving Rubik's Cube Without Tricky Sampling—0
Supervised Learning-enhanced Multi-Group Actor Critic for Live Stream Allocation in FeedCode0
TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning—0
Convex Regularization and Convergence of Policy Gradient Flows under Safety Constraints—0
A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges—0
Dynamic Non-Prehensile Object Transport via Model-Predictive Reinforcement Learning—0
ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics—0
ScaleViz: Scaling Visualization Recommendation Models on Large Data—0
NeoHebbian Synapses to Accelerate Online Training of Neuromorphic Hardware—0
Dynamic Retail Pricing via Q-Learning -- A Reinforcement Learning Framework for Enhanced Revenue Management—0
PROGRESSOR: A Perceptually Guided Reward Estimator with Self-Supervised Online Refinement—0
Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative TradingCode2
Accelerating Proximal Policy Optimization Learning Using Task Prediction for Solving Environments with Delayed Rewards—0
LLM-Based Offline Learning for Embodied Agents via Consistency-Guided Reward Ensemble—0
Free^2Guide: Gradient-Free Path Integral Control for Enhancing Text-to-Video Generation with Large Vision-Language Models—0
M3: Mamba-assisted Multi-Circuit Optimization via MBRL with Effective Scheduling—0
Probing for Consciousness in Machines—0
Unsupervised Event Outlier Detection in Continuous Time—0
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Benchmark Results

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