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 6326–6350 of 15113 papers

TitleStatusHype
Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation—0
Look Harder: A Neural Machine Translation Model with Hard Attention—0
LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots—0
LORD: Large Models based Opposite Reward Design for Autonomous Driving—0
Loss- and Reward-Weighting for Efficient Distributed Reinforcement Learning—0
Loss Functions for Multiset Prediction—0
Loss is its own Reward: Self-Supervision for Reinforcement Learning—0
Loss of Plasticity in Continual Deep Reinforcement Learning—0
Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems—0
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics—0
Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces—0
Low Dose CT Denoising via Joint Bilateral Filtering and Intelligent Parameter Optimization—0
Low Dose Helical CBCT denoising by using domain filtering with deep reinforcement learning—0
Low Emission Building Control with Zero-Shot Reinforcement Learning—0
Low Entropy Communication in Multi-Agent Reinforcement Learning—0
Lower Bounds for Learning in Revealing POMDPs—0
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning—0
Low-level Pose Control of Tilting Multirotor for Wall Perching Tasks Using Reinforcement Learning—0
Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery—0
Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing—0
1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities—0
Deploying Offline Reinforcement Learning with Human Feedback—0
Deploying Reinforcement Learning in Water Transport—0
Depth and nonlinearity induce implicit exploration for RL—0
Depth-Constrained ASV Navigation with Deep RL and Limited Sensing—0
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Benchmark Results

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