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 6951–6975 of 15113 papers

TitleStatusHype
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
A Look at Value-Based Decision-Time vs. Background Planning Methods Across Different Settings—0
Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration—0
Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization—0
Understanding & Generalizing AlphaGo Zero—0
Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspective—0
The Importance of Online Data: Understanding Preference Fine-tuning via Coverage—0
Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization—0
Understanding Self-Predictive Learning for Reinforcement Learning—0
Understanding the Complexity Gains of Single-Task RL with a Curriculum—0
Understanding the Generalization Gap in Visual Reinforcement Learning—0
Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning—0
Understanding the Pathologies of Approximate Policy Evaluation when Combined with Greedification in Reinforcement Learning—0
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Benchmark Results

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