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 6226–6250 of 15113 papers

TitleStatusHype
Black-box Optimizer with Implicit Natural Gradient—0
Stochastic Intervention for Causal Inference via Reinforcement Learning—0
Stochastic Inverse Reinforcement Learning—0
Stochastic Inverse Reinforcement Learning—0
Stochastic Learning Approach to Binary Optimization for Optimal Design of Experiments—0
Stochastic Lipschitz Q-Learning—0
Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning—0
Stochastic Q-learning for Large Discrete Action Spaces—0
Stochastic Reinforcement Learning—0
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function—0
Stochastic Variance Reduction for Deep Q-learning—0
Stochastic Variance Reduction for Policy Gradient Estimation—0
Stochastic Variance Reduction Methods for Policy Evaluation—0
Stock market microstructure inference via multi-agent reinforcement learning—0
Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength—0
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization—0
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL—0
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning—0
Story Shaping: Teaching Agents Human-like Behavior with Stories—0
Straight to the point: reinforcement learning for user guidance in ultrasound—0
Strategically Linked Decisions in Long-Term Planning and Reinforcement Learning—0
Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents—0
Strategic bidding in freight transport using deep reinforcement learning—0
Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination—0
Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning—0
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Benchmark Results

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