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 11176–11200 of 15113 papers

TitleStatusHype
Unbiased Weight Maximization—0
Uncertainty-aware Contact-safe Model-based Reinforcement Learning—0
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments—0
Uncertainty-aware Distributional Offline Reinforcement Learning—0
Uncertainty-aware Low-Rank Q-Matrix Estimation for Deep Reinforcement Learning—0
Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving—0
Uncertainty-Aware Reinforcement Learning for Collision Avoidance—0
Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning—0
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking—0
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning—0
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning—0
Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables—0
Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs—0
Uncertainty Estimation for Language Reward Models—0
Uncertainty quantification for Markov chains with application to temporal difference learning—0
Uncertainty Regularized Policy Learning for Offline Reinforcement Learning—0
Uncertainty Weighted Offline Reinforcement Learning—0
Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis—0
Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings—0
Understanding and Leveraging Overparameterization in Recursive Value Estimation—0
Understanding and Leveraging Causal Relations in Deep Reinforcement Learning—0
Understanding and Preventing Capacity Loss in Reinforcement Learning—0
Understanding and Shifting Preferences for Battery Electric Vehicles—0
Understanding and Simplifying One-Shot Architecture Search—0
Optimality theory of stigmergic collective information processing by chemotactic cells—0
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Benchmark Results

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