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 9776–9800 of 15113 papers

TitleStatusHype
Alpha-DAG: a reinforcement learning based algorithm to learn Directed Acyclic Graphs—0
Guiding Representation Learning in Deep Generative Models with Policy Gradients—0
Deep Reinforcement Learning-based Anti-jamming Power Allocation in a Two-cell NOMA Network—0
Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning—0
FactoredRL: Leveraging Factored Graphs for Deep Reinforcement Learning—0
Explore with Dynamic Map: Graph Structured Reinforcement Learning—0
Factored Action Spaces in Deep Reinforcement Learning—0
Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets—0
Exploring Transferability of Perturbations in Deep Reinforcement Learning—0
Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning—0
Curriculum-based Deep Reinforcement Learning for Quantum Control—0
Towards Understanding Asynchronous Advantage Actor-critic: Convergence and Linear Speedup—0
Autonomous Maintenance in IoT Networks via AoI-driven Deep Reinforcement Learning—0
Toward Reliable Designs of Data-Driven Reinforcement Learning Tracking Control for Euler-Lagrange Systems—0
Relational Deep Reinforcement Learning for Routing in Wireless Networks—0
Model Free Reinforcement Learning Algorithm for Stationary Mean field Equilibrium for Multiple Types of Agents—0
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Is Pessimism Provably Efficient for Offline RL?—0
LISPR: An Options Framework for Policy Reuse with Reinforcement Learning—0
Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing—0
Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy—0
Portfolio Optimization with 2D Relative-Attentional Gated Transformer—0
POPO: Pessimistic Offline Policy OptimizationCode0
Towards sample-efficient episodic control with DAC-ML—0
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Benchmark Results

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