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 11251–11275 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization—0
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning—0
Learning Context-aware Task Reasoning for Efficient Meta-reinforcement Learning—0
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Cluster-Based Social Reinforcement Learning—0
Adaptive Structural Hyper-Parameter Configuration by Q-Learning—0
Formal Controller Synthesis for Continuous-Space MDPs via Model-Free Reinforcement Learning—0
Gaussian Process Policy Optimization—0
Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robots—0
Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph—0
Dynamic Queue-Jump Lane for Emergency Vehicles under Partially Connected Settings: A Multi-Agent Deep Reinforcement Learning Approach—0
Real-World Human-Robot Collaborative Reinforcement Learning—0
Risk-Averse Learning by Temporal Difference Methods—0
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss—0
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement LearningCode0
Fully Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks—0
Learning Near Optimal Policies with Low Inherent Bellman Error—0
Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts—0
TAdam: A Robust Stochastic Gradient OptimizerCode0
A Self-Tuning Actor-Critic Algorithm—0
Deep Reinforcement Learning for FlipIt Security Game—0
Mixed Reinforcement Learning with Additive Stochastic Uncertainty—0
Reinforcement Learning through Active Inference—0
On Catastrophic Interference in Atari 2600 GamesCode0
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Benchmark Results

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