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 9851–9875 of 15113 papers

TitleStatusHype
Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World—0
SUMBT+LaRL: Effective Multi-domain End-to-end Neural Task-oriented Dialog System—0
A Centralised Soft Actor Critic Deep Reinforcement Learning Approach to District Demand Side Management through CityLearnCode0
Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management—0
Deep Reinforcement Learning for On-line Dialogue State Tracking—0
Is Q-Learning Provably Efficient? An Extended Analysis—0
Contextual Bandits for adapting to changing User preferences over time—0
DISPATCH: Design Space Exploration of Cyber-Physical Systems—0
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion—0
Learn to Exceed: Stereo Inverse Reinforcement Learning with Concurrent Policy Optimization—0
Dynamic Horizon Value Estimation for Model-based Reinforcement Learning—0
Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning—0
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue SystemsCode0
Mobile Cellular-Connected UAVs: Reinforcement Learning for Sky Limits—0
Deep Reinforcement Learning Methods for Structure-Guided Processing Path OptimizationCode0
RL STaR Platform: Reinforcement Learning for Simulation based Training of RobotsCode1
Reinforcement Learning Approaches in Social Robotics—0
Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control—0
Multiplayer Support for the Arcade Learning Environment—0
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms—0
Construction of Polar Codes with Reinforcement Learning—0
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control—0
Private Reinforcement Learning with PAC and Regret Guarantees—0
Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos—0
Efficient Reinforcement Learning Development with RLzoo—0
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Benchmark Results

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