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 9226–9250 of 15113 papers

TitleStatusHype
Reinforcement Learning from Bagged Reward—0
Reinforcement Learning from Demonstrations by Novel Interactive Expert and Application to Automatic Berthing Control Systems for Unmanned Surface Vessel—0
Reinforcement Learning from Diverse Human Preferences—0
Reinforcement Learning from Imperfect Demonstrations—0
Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewards—0
Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance—0
Reinforcement Learning from LLM Feedback to Counteract Goal Misgeneralization—0
Reinforcement Learning-Guided Semi-Supervised Learning—0
Reinforcement Learning in 20Q Game with Generic Knowledge Bases—0
Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space—0
Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior—0
Reinforcement Learning in a large scale photonic Recurrent Neural Network—0
Reinforcement Learning in a Neurally Controlled Robot Using Dopamine Modulated STDP—0
Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization—0
Reinforcement Learning in Categorical Cybernetics—0
Reinforcement Learning in Computing and Network Convergence Orchestration—0
Reinforcement Learning in Conflicting Environments for Autonomous Vehicles—0
Reinforcement Learning in Economics and Finance—0
Reinforcement Learning in Education: A Multi-Armed Bandit Approach—0
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions—0
Deep Reinforcement Learning for FlipIt Security Game—0
Reinforcement learning informed evolutionary search for autonomous systems testing—0
Reinforcement Learning in Healthcare: A Survey—0
Reinforcement Learning in Hyperbolic Spaces: Models and Experiments—0
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation—0
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Benchmark Results

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