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 7926–7950 of 15113 papers

TitleStatusHype
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization—0
On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples—0
On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning—0
On the Search for Feedback in Reinforcement Learning—0
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems—0
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning—0
On the Statistical Complexity for Offline and Low-Adaptive Reinforcement Learning with Structures—0
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL—0
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization—0
On the Theory of Reinforcement Learning with Once-per-Episode Feedback—0
On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning—0
On the use of feature-maps and parameter control for improved quality-diversity meta-evolution—0
On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer—0
On the Weaknesses of Reinforcement Learning for Neural Machine Translation—0
On Thompson Sampling for Smoother-than-Lipschitz Bandits—0
Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments—0
On Trade-offs of Image Prediction in Visual Model-Based Reinforcement Learning—0
On Training Flexible Robots using Deep Reinforcement Learning—0
On Transforming Reinforcement Learning by Transformer: The Development Trajectory—0
On Value Functions and the Agent-Environment Boundary—0
On Wasserstein Reinforcement Learning and the Fokker-Planck equation—0
OPAC: Opportunistic Actor-Critic—0
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning—0
OPEB: Open Physical Environment Benchmark for Artificial Intelligence—0
Open-Ended Learning Strategies for Learning Complex Locomotion Skills—0
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Benchmark Results

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