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 8851–8875 of 15113 papers

TitleStatusHype
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers—0
MASAI: Multi-agent Summative Assessment Improvement for Unsupervised Environment DesignCode0
Tangent Space Least Adaptive Clustering—0
Representation Learning for Out-of-distribution Generalization in Reinforcement Learning—0
Disentangled Predictive Representation for Meta-Reinforcement Learning—0
Density-Based Bonuses on Learned Representations for Reward-Free Exploration in Deep Reinforcement Learning—0
Bellman-consistent Pessimism for Offline Reinforcement Learning—0
Learning Task-Relevant Representations with Selective Contrast for Reinforcement Learning in a Real-World Application—0
Exploration-Driven Representation Learning in Reinforcement Learning—0
Data-Efficient Exploration with Self Play for Atari—0
A new soft computing method for integration of expert's knowledge in reinforcement learn-ing problems—0
Learning to Explore Multiple Environments without Rewards—0
Learning on Abstract Domains: A New Approach for Verifiable Guarantee in Reinforcement Learning—0
Model-free Reinforcement Learning for Branching Markov Decision Processes—0
Taylor Expansion of Discount Factors—0
Safe Reinforcement Learning with Linear Function Approximation—0
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs—0
Offline Reinforcement Learning as Anti-Exploration—0
Automatic Risk Adaptation in Distributional Reinforcement Learning—0
Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning—0
A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning—0
GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning—0
DECORE: Deep Compression with Reinforcement Learning—0
Corruption-Robust Offline Reinforcement Learning—0
Hyperspace Neighbor Penetration Approach to Dynamic Programming for Model-Based Reinforcement Learning Problems with Slowly Changing Variables in A Continuous State Space—0
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Benchmark Results

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