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 12301–12350 of 15113 papers

TitleStatusHype
Proximal Distilled Evolutionary Reinforcement LearningCode0
Optimal Use of Experience in First Person Shooter Environments—0
Modern Deep Reinforcement Learning AlgorithmsCode0
Ranking Policy GradientCode0
Neural networks with motivation—0
Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations—0
A neurally plausible model learns successor representations in partially observable environmentsCode0
A Story of Two Streams: Reinforcement Learning Models from Human Behavior and NeuropsychiatryCode1
Split Q Learning: Reinforcement Learning with Two-Stream RewardsCode1
Revised Progressive-Hedging-Algorithm Based Two-layer Solution Scheme for Bayesian Reinforcement Learning—0
Reinforcement Learning with Convex ConstraintsCode1
Shaping Belief States with Generative Environment Models for RL—0
A Study of State Aliasing in Structured Prediction with RNNs—0
Disentangled Skill Embeddings for Reinforcement Learning—0
Leveraging Reinforcement Learning Techniques for Effective Policy Adoption and Validation—0
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction—0
Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach—0
A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement LearningCode0
Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning—0
Cooperative Lane Changing via Deep Reinforcement Learning—0
A Deep Reinforcement Learning Approach for Global RoutingCode0
Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine LearningCode0
When Multiple Agents Learn to Schedule: A Distributed Radio Resource Management Framework—0
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks—0
Unsupervised Learning of Object Keypoints for Perception and ControlCode1
Experience Replay Optimization—0
Calibrated Model-Based Deep Reinforcement LearningCode0
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study—0
Wasserstein Adversarial Imitation Learning—0
When to Trust Your Model: Model-Based Policy OptimizationCode1
Reward Prediction Error as an Exploration Objective in Deep RL—0
Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing—0
Directed Exploration for Reinforcement Learning—0
Hill Climbing on Value Estimates for Search-control in Dyna—0
Language as an Abstraction for Hierarchical Deep Reinforcement LearningCode0
Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning—0
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination—0
Robust Reinforcement Learning for Continuous Control with Model Misspecification—0
Towards White-box Benchmarks for Algorithm Control—0
Sample-efficient Adversarial Imitation Learning from Observation—0
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration—0
Universal Successor Features Based Deep Reinforcement Learning for Navigation—0
A Joint Planning and Learning Framework for Human-Aided Decision-Making—0
LPaintB: Learning to Paint from Self-Supervision—0
Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computer—0
Learning-Driven Exploration for Reinforcement LearningCode0
A gray-box approach for curriculum learning—0
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement LearningCode1
Reinforcement Learning Driven Heuristic Optimization—0
Reinforcement Learning with Non-uniform State Representations for Adaptive Search—0
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Benchmark Results

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