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 3251–3260 of 15113 papers

TitleStatusHype
QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust ControlCode2
Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Real-Time Network-Level Traffic Signal Control: An Explicit Multiagent Coordination Method—0
Datasets and Benchmarks for Offline Safe Reinforcement LearningCode2
Off-policy Evaluation in Doubly Inhomogeneous EnvironmentsCode0
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources—0
A reinforcement learning strategy for p-adaptation in high order solvers—0
Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning—0
Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning—0
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Benchmark Results

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