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 6476–6500 of 15113 papers

TitleStatusHype
Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning—0
Discovery of Options via Meta-Learned Subgoals—0
Discovery of Useful Questions as Auxiliary Tasks—0
Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning—0
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning—0
Smaller World Models for Reinforcement Learning—0
Discrete linear-complexity reinforcement learning in continuous action spaces for Q-learning algorithms—0
Discrete MDL Predicts in Total Variation—0
Discrete Predictive Representation for Long-horizon Planning—0
Discrete-Time Mean Field Control with Environment States—0
Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning—0
Discriminator Augmented Model-Based Reinforcement Learning—0
Disentangled Predictive Representation for Meta-Reinforcement Learning—0
Disentangled Skill Embeddings for Reinforcement Learning—0
Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning—0
Disentangling causal effects for hierarchical reinforcement learning—0
Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the World—0
Disentangling Controllable Object through Video Prediction Improves Visual Reinforcement Learning—0
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction—0
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning—0
Disentangling Generalization in Reinforcement Learning—0
Disentangling Options with Hellinger Distance Regularizer—0
Disentangling Recognition and Decision Regrets in Image-Based Reinforcement Learning—0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning—0
Disentangling Transfer in Continual Reinforcement Learning—0
Show:102550
← PrevPage 260 of 605Next →

Benchmark Results

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