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 6126–6150 of 15113 papers

TitleStatusHype
Learning to Play Pong using Policy Gradient Learning—0
Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World—0
Learning to Play Table Tennis From Scratch using Muscular Robots—0
Learning to Play Two-Player Perfect-Information Games without Knowledge—0
Learning to predict where to look in interactive environments using deep recurrent q-learning—0
Learning to Program Variational Quantum Circuits with Fast Weights—0
Learning to Progressively Plan—0
Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems—0
Learning to Prune Deep Neural Networks via Reinforcement Learning—0
Learning to Query Internet Text for Informing Reinforcement Learning Agents—0
Learning to Reach Goals Without Reinforcement Learning—0
Learning to Reason: Distilling Hierarchy via Self-Supervision and Reinforcement Learning—0
Learning to Reason in Large Theories without Imitation—0
Learning to Recover Sparse Signals—0
Learning to Reinforcement Learn by Imitation—0
Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning—0
Learning to Represent Action Values as a Hypergraph on the Action Vertices—0
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games—0
Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning—0
Learning Torque Control for Quadrupedal Locomotion—0
Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning—0
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data—0
Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments—0
Learning to sample in Cartesian MRI—0
Learning to Sample with Local and Global Contexts in Experience Replay Buffer—0
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Benchmark Results

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