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

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Showing 2081–2090 of 15113 papers

TitleStatusHype
Benchmarking Reinforcement Learning Techniques for Autonomous NavigationCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Constrained episodic reinforcement learning in concave-convex and knapsack settingsCode1
Constrained Policy Optimization via Bayesian World ModelsCode1
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement LearningCode1
Constructions in combinatorics via neural networksCode1
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
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Benchmark Results

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