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 6151–6175 of 15113 papers

TitleStatusHype
SR-Reward: Taking The Path More Traveled—0
Stability and Generalisation in Batch Reinforcement Learning—0
Stability-Certified Reinforcement Learning via Spectral Normalization—0
Stability-certified reinforcement learning: A control-theoretic perspective—0
Stability Constrained Reinforcement Learning for Real-Time Voltage Control—0
Stability of Stochastic Approximations with `Controlled Markov' Noise and Temporal Difference Learning—0
Stability-Preserving Automatic Tuning of PID Control with Reinforcement Learning—0
Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning—0
Stabilizing Off-Policy Reinforcement Learning with Conservative Policy Gradients—0
Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation—0
Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion—0
Stabilizing Transformer-Based Action Sequence Generation For Q-Learning—0
Stabilizing Unsupervised Environment Design with a Learned Adversary—0
Stabilizing Visual Reinforcement Learning via Asymmetric Interactive Cooperation—0
Stable and Efficient Policy Evaluation—0
Stable Continual Reinforcement Learning via Diffusion-based Trajectory Replay—0
Stable deep reinforcement learning method by predicting uncertainty in rewards as a subtask—0
Stable Modular Control via Contraction Theory for Reinforcement Learning—0
Stable Reinforcement Learning for Optimal Frequency Control: A Distributed Averaging-Based Integral Approach—0
Stable Reinforcement Learning with Unbounded State Space—0
Stable Relay Learning Optimization Approach for Fast Power System Production Cost Minimization Simulation—0
Stackelberg Batch Policy Learning—0
Staged Reinforcement Learning for Complex Tasks through Decomposed Environments—0
Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem—0
StaQ it! Growing neural networks for Policy Mirror Descent—0
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Benchmark Results

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