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 9751–9775 of 15113 papers

TitleStatusHype
Constrained Reinforcement Learning With Learned Constraints—0
Divide-and-Conquer Monte Carlo Tree Search—0
A Survey on Deep Reinforcement Learning for Audio-Based Applications—0
Improving Learning to Branch via Reinforcement Learning—0
Interpretable Meta-Reinforcement Learning with Actor-Critic Method—0
Hierarchical Meta Reinforcement Learning for Multi-Task EnvironmentsCode0
CAT-SAC: Soft Actor-Critic with Curiosity-Aware Entropy Temperature—0
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay—0
Deep Coherent Exploration For Continuous Control—0
Interpretable Reinforcement Learning With Neural Symbolic Logic—0
Intrinsically Guided Exploration in Meta Reinforcement Learning—0
Addressing Extrapolation Error in Deep Offline Reinforcement Learning—0
Adaptive Learning Rates for Multi-Agent Reinforcement Learning—0
A Reduction Approach to Constrained Reinforcement Learning—0
Explainable Reinforcement Learning Through Goal-Based Explanations—0
Adaptive Multi-model Fusion Learning for Sparse-Reward Reinforcement Learning—0
Batch Reinforcement Learning Through Continuation Method—0
Grounding Language to Entities for Generalization in Reinforcement Learning—0
Communication in Multi-Agent Reinforcement Learning: Intention Sharing—0
Attention-driven Robotic Manipulation—0
Explicit Pareto Front Optimization for Constrained Reinforcement Learning—0
Hellinger Distance Constrained Regression—0
Combining Imitation and Reinforcement Learning with Free Energy Principle—0
Invariant Representations for Reinforcement Learning without Reconstruction—0
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection ApproachCode0
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Benchmark Results

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