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 12751–12800 of 15113 papers

TitleStatusHype
Learning to Decompose Compound Questions with Reinforcement Learning—0
Backplay: 'Man muss immer umkehren'—0
Automata Guided Skill Composition—0
Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning—0
Deep reinforcement learning with relational inductive biases—0
Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning—0
A Guider Network for Multi-Dual Learning—0
Efficient Model-free Reinforcement Learning in Metric SpacesCode0
Learning Heuristics for Automated Reasoning through Reinforcement Learning—0
Learning Goal-Conditioned Value Functions with one-step Path rewards rather than Goal-Rewards—0
A new dog learns old tricks: RL finds classic optimization algorithms—0
Learning to Progressively Plan—0
Learning to Reinforcement Learn by Imitation—0
Information-Theoretic Considerations in Batch Reinforcement Learning—0
Inducing Cooperation via Learning to reshape rewards in semi-cooperative multi-agent reinforcement learning—0
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach—0
Learning agents with prioritization and parameter noise in continuous state and action space—0
Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving—0
Learning Actionable Representations with Goal Conditioned Policies—0
DHER: Hindsight Experience Replay for Dynamic GoalsCode0
ACTRCE: Augmenting Experience via Teacher’s Advice—0
Explicit Recall for Efficient Exploration—0
SIMILE: Introducing Sequential Information towards More Effective Imitation Learning—0
Soft Q-Learning with Mutual-Information Regularization—0
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning—0
Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning—0
Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis—0
SUPERVISED POLICY UPDATECode0
Predicted Variables in Programming—0
NEURAL MALWARE CONTROL WITH DEEP REINFORCEMENT LEARNING—0
Understanding & Generalizing AlphaGo Zero—0
Visceral Machines: Reinforcement Learning with Intrinsic Physiological Rewards—0
Recurrent Experience Replay in Distributed Reinforcement LearningCode0
Towards Consistent Performance on Atari using Expert Demonstrations—0
Rating Continuous Actions in Spatial Multi-Agent Problems—0
Modeling the Long Term Future in Model-Based Reinforcement Learning—0
Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning—0
Argus: Smartphone-enabled Human Cooperation via Multi-Agent Reinforcement Learning for Disaster Situational Awareness—0
Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage—0
RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape CompletionCode0
Deep Neuroevolution of Recurrent and Discrete World ModelsCode0
Arbitrage of Energy Storage in Electricity Markets with Deep Reinforcement Learning—0
Self Training Autonomous Driving Agent—0
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning—0
Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban EnvironmentsCode0
Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks—0
Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning FrameworkCode0
Cognitive Radar Using Reinforcement Learning in Automotive Applications—0
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning—0
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning—0
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Benchmark Results

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