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 8651–8675 of 15113 papers

TitleStatusHype
Pseudo-Model-Free Hedging for Variable Annuities via Deep Reinforcement Learning—0
Pseudorehearsal in actor-critic agents—0
Pseudorehearsal in actor-critic agents with neural network function approximation—0
Pseudorehearsal in value function approximation—0
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning—0
Reinforcement Learning and its Connections with Neuroscience and Psychology—0
Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics—0
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning—0
Purpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?—0
Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers—0
PWM: Policy Learning with Multi-Task World Models—0
Q-Cogni: An Integrated Causal Reinforcement Learning Framework—0
Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning—0
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration—0
QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine—0
QF-tuner: Breaking Tradition in Reinforcement Learning—0
Qgraph-bounded Q-learning: Stabilizing Model-Free Off-Policy Deep Reinforcement Learning—0
Q-greedyUCB: a New Exploration Policy for Adaptive and Resource-efficient Scheduling—0
Efficient Off-Policy Reinforcement Learning via Brain-Inspired Computing—0
QKSA: Quantum Knowledge Seeking Agent -- resource-optimized reinforcement learning using quantum process tomography—0
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes—0
Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells—0
Q-Learning Based Aerial Base Station Placement for Fairness Enhancement in Mobile Networks—0
Q-learning Based Optimal False Data Injection Attack on Probabilistic Boolean Control Networks—0
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL—0
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Benchmark Results

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