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 10051–10075 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning of Transition States—0
Robust Quadruped Jumping via Deep Reinforcement Learning—0
Scaffolding Reflection in Reinforcement Learning Framework for Confinement Escape Problem—0
Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity—0
Phoebe: Reuse-Aware Online Caching with Reinforcement Learning for Emerging Storage Models—0
Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee—0
Robotic self-representation improves manipulation skills and transfer learning—0
Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning AgentsCode0
Self-supervised reinforcement learning for speaker localisation with the iCub humanoid robot—0
Steady State Analysis of Episodic Reinforcement Learning—0
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges—0
Imposing Robust Structured Control Constraint on Reinforcement Learning of Linear Quadratic Regulator—0
Hierarchical reinforcement learning for efficient exploration and transfer—0
Griddly: A platform for AI research in games—0
Adaptive Neural Architectures for Recommender Systems—0
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension—0
CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee—0
Behaviorally Diverse Traffic Simulation via Reinforcement Learning—0
Proximal Policy Optimization via Enhanced Exploration Efficiency—0
Reinforcement Learning with Dual-Observation for General Video Game PlayingCode0
Non-local Optimization: Imposing Structure on Optimization Problems by Relaxation—0
Reinforcement Learning with Time-dependent Goals for Robotic Musicians—0
Reinforcement Learning Experiments and Benchmark for Solving Robotic Reaching TasksCode0
Offline Learning of Counterfactual Predictions for Real-World Robotic Reinforcement Learning—0
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms—0
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Benchmark Results

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