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

Showing 98019850 of 15113 papers

TitleStatusHype
Model-Free Reinforcement Learning for Asset Allocation0
Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation0
Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specifications0
Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization0
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity0
Model-free Representation Learning and Exploration in Low-rank MDPs0
Model-Free Risk-Sensitive Reinforcement Learning0
Model-Free RL Agents Demonstrate System 1-Like Intentionality0
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees0
Model-Free Unsupervised Learning for Optimization Problems with Constraints0
Model Generation with Provable Coverability for Offline Reinforcement Learning0
Model Imitation for Model-Based Reinforcement Learning0
Modeling Adaptive Platoon and Reservation Based Autonomous Intersection Control: A Deep Reinforcement Learning Approach0
Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning0
Modeling Cyber-Physical Human Systems via an Interplay Between Reinforcement Learning and Game Theory0
Modeling Fake News in Social Networks with Deep Multi-Agent Reinforcement Learning0
CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention0
Modeling Human Reading with Neural Attention0
Modeling human road crossing decisions as reward maximization with visual perception limitations0
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance0
Modeling Mobile Health Users as Reinforcement Learning Agents0
Modeling Others using Oneself in Multi-Agent Reinforcement Learning0
Modeling Risk in Reinforcement Learning: A Literature Mapping0
Modeling Sensorimotor Coordination as Multi-Agent Reinforcement Learning with Differentiable Communication0
Modeling Survival in model-based Reinforcement Learning0
Modeling the Formation of Social Conventions from Embodied Real-Time Interactions0
Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning0
Modeling the Long Term Future in Model-Based Reinforcement Learning0
Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning0
Model-Invariant State Abstractions for Model-Based Reinforcement Learning0
Model Inversion Attacks against Graph Neural Networks0
ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control0
Modelling Cooperation in Network Games with Spatio-Temporal Complexity0
Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer0
Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning0
Modelling resource allocation in uncertain system environment through deep reinforcement learning0
Modelling Stock-market Investors as Reinforcement Learning Agents [Correction]0
Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG0
Modelling Working Memory using Deep Recurrent Reinforcement Learning0
Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learning0
Model-predictive control and reinforcement learning in multi-energy system case studies0
Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming0
Model Predictive Control via On-Policy Imitation Learning0
Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties0
Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles0
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol0
Model Selection in Reinforcement Learning with General Function Approximations0
Model Selection for Generic Reinforcement Learning0
Modified Actor-Critics0
Modifying RL Policies with Imagined Actions: How Predictable Policies Can Enable Users to Perform Novel Tasks0
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Benchmark Results

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