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 3781–3790 of 15113 papers

TitleStatusHype
Confidence Aware Inverse Constrained Reinforcement LearningCode0
Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems—0
Reinforcement Learning via Auxiliary Task DistillationCode0
OCALM: Object-Centric Assessment with Language Models—0
Enhancing Commentary Strategies for Imperfect Information Card Games: A Study of Large Language Models in Guandan CommentaryCode0
Diffusion Spectral Representation for Reinforcement Learning—0
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World ModelsCode0
Multistep Criticality Search and Power Shaping in Microreactors with Reinforcement Learning—0
Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning—0
SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement LearningCode0
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Benchmark Results

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