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 8001–8025 of 15113 papers

TitleStatusHype
Density-Based Bonuses on Learned Representations for Reward-Free Exploration in Deep Reinforcement Learning—0
Exploration-Driven Representation Learning in Reinforcement Learning—0
Learning Task-Relevant Representations with Selective Contrast for Reinforcement Learning in a Real-World Application—0
Data-Efficient Exploration with Self Play for Atari—0
Disentangled Predictive Representation for Meta-Reinforcement Learning—0
Learning to Explore Multiple Environments without Rewards—0
Deep Reinforcement Learning based Group Recommender SystemCode1
A new soft computing method for integration of expert's knowledge in reinforcement learn-ing problems—0
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Learning on Abstract Domains: A New Approach for Verifiable Guarantee in Reinforcement Learning—0
Bellman-consistent Pessimism for Offline Reinforcement Learning—0
A Game-Theoretic Approach to Multi-Agent Trust Region OptimizationCode1
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor RepresentationCode1
A Minimalist Approach to Offline Reinforcement LearningCode1
Model-free Reinforcement Learning for Branching Markov Decision Processes—0
Recomposing the Reinforcement Learning Building Blocks with HypernetworksCode1
Corruption-Robust Offline Reinforcement Learning—0
A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning—0
DECORE: Deep Compression with Reinforcement Learning—0
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement LearningCode2
GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning—0
Automatic Risk Adaptation in Distributional Reinforcement Learning—0
Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning—0
Taylor Expansion of Discount Factors—0
Offline Reinforcement Learning as Anti-Exploration—0
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Benchmark Results

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