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 7251–7275 of 15113 papers

TitleStatusHype
Extendable NFV-Integrated Control Method Using Reinforcement Learning—0
Extend Adversarial Policy Against Neural Machine Translation via Unknown Token—0
Extended Radial Basis Function Controller for Reinforcement Learning—0
Extending a Quantum Reinforcement Learning Exploration Policy with Flags to Connect Four—0
Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making—0
External control of a genetic toggle switch via Reinforcement Learning—0
EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data—0
Extracting Action Sequences from Texts Based on Deep Reinforcement Learning—0
Extracting Expert's Goals by What-if Interpretable Modeling—0
Extracting Latent State Representations with Linear Dynamics from Rich Observations—0
Extrapolation in Gridworld Markov-Decision Processes—0
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory—0
Extreme State Aggregation Beyond MDPs—0
Extreme Value Monte Carlo Tree Search—0
ExWarp: Extrapolation and Warping-based Temporal Supersampling for High-frequency Displays—0
Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents—0
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning—0
FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots—0
Face valuing: Training user interfaces with facial expressions and reinforcement learning—0
Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework—0
Factored Action Spaces in Deep Reinforcement Learning—0
Factored Adaptation for Non-Stationary Reinforcement Learning—0
FactoredRL: Leveraging Factored Graphs for Deep Reinforcement Learning—0
Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning—0
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Benchmark Results

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