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 8451–8475 of 15113 papers

TitleStatusHype
On Memory Mechanism in Multi-Agent Reinforcement Learning—0
On Modeling Long-Term User Engagement from Stochastic Feedback—0
On Multi-Agent Learning in Team Sports Games—0
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning—0
On Neural Consolidation for Transfer in Reinforcement Learning—0
Towards Tractable Optimism in Model-Based Reinforcement Learning—0
On Optimistic versus Randomized Exploration in Reinforcement Learning—0
On Oracle-Efficient PAC RL with Rich Observations—0
On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making—0
On overfitting and asymptotic bias in batch reinforcement learning with partial observability—0
On-policy Actor-Critic Reinforcement Learning for Multi-UAV Exploration—0
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion—0
On Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation—0
On-Policy Model Errors in Reinforcement Learning—0
On-Policy Optimization with Group Equivalent Preference for Multi-Programming Language Understanding—0
On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling—0
Policy Optimization Reinforcement Learning with Entropy Regularization—0
On-Policy Robot Imitation Learning from a Converging Supervisor—0
On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm—0
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning—0
On Reinforcement Learning, Effect Handlers, and the State Monad—0
On Reinforcement Learning for Full-length Game of StarCraft—0
On Reinforcement Learning for Turn-based Zero-sum Markov Games—0
On Representation Complexity of Model-based and Model-free Reinforcement Learning—0
On Reward-Free Reinforcement Learning with Linear Function Approximation—0
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Benchmark Results

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