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 6026–6050 of 15113 papers

TitleStatusHype
Learning Sparse Representations in Reinforcement Learning with Sparse Coding—0
Learning sparse representations in reinforcement learning—0
Learning Sparse Representations Incrementally in Deep Reinforcement Learning—0
Learning State Representations for Query Optimization with Deep Reinforcement Learning—0
Learning State Representations in Complex Systems with Multimodal Data—0
Learning State Representations via Temporal Cycle-Consistency Constraint in Model-Based Reinforcement Learning—0
Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization—0
Learning Structured Communication for Multi-agent Reinforcement Learning—0
Learning swimming escape patterns for larval fish under energy constraints—0
Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior—0
Learning Symbolic Rules for Interpretable Deep Reinforcement Learning—0
Learning Task Automata for Reinforcement Learning using Hidden Markov Models—0
Learning Task-Driven Control Policies via Information Bottlenecks—0
Learning Task Informed Abstractions—0
Learning Task-Relevant Representations with Selective Contrast for Reinforcement Learning in a Real-World Application—0
Learning Task Sampling Policy for Multitask Learning—0
Learning Temporal Abstraction with Information-theoretic Constraints for Hierarchical Reinforcement Learning—0
Learning Temporally Extended Skills in Continuous Domains as Symbolic Actions for Planning—0
Learning Temporal Point Processes via Reinforcement Learning—0
Learning the Arrow of Time for Problems in Reinforcement Learning—0
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning—0
Learning the model-free linear quadratic regulator via random search—0
Learning the policy for mixed electric platoon control of automated and human-driven vehicles at signalized intersection: a random search approach—0
Learning the Target Network in Function Space—0
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas—0
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Benchmark Results

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