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 12601–12650 of 15113 papers

TitleStatusHype
The Game of Tetris in Machine LearningCode0
A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications—0
Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning—0
Hierarchical Policy Learning is Sensitive to Goal Space Design—0
Deep Residual Reinforcement Learning—0
Meta-learners' learning dynamics are unlike learners'—0
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach—0
Collaborative Evolutionary Reinforcement LearningCode0
Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning—0
Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving—0
Efficient Model-free Reinforcement Learning in Metric SpacesCode0
Information-Theoretic Considerations in Batch Reinforcement Learning—0
Modeling the Long Term Future in Model-Based Reinforcement Learning—0
Visceral Machines: Reinforcement Learning with Intrinsic Physiological Rewards—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
Understanding & Generalizing AlphaGo Zero—0
Recurrent Experience Replay in Distributed Reinforcement LearningCode0
Towards Consistent Performance on Atari using Expert Demonstrations—0
SIMILE: Introducing Sequential Information towards More Effective Imitation Learning—0
NEURAL MALWARE CONTROL WITH DEEP REINFORCEMENT LEARNING—0
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning—0
Explicit Recall for Efficient Exploration—0
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach—0
Learning to Progressively Plan—0
Learning to Decompose Compound Questions with Reinforcement Learning—0
Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning—0
ACTRCE: Augmenting Experience via Teacher’s Advice—0
Inducing Cooperation via Learning to reshape rewards in semi-cooperative multi-agent reinforcement learning—0
Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning—0
Learning Goal-Conditioned Value Functions with one-step Path rewards rather than Goal-Rewards—0
Deep reinforcement learning with relational inductive biases—0
Learning agents with prioritization and parameter noise in continuous state and action space—0
Learning Actionable Representations with Goal Conditioned Policies—0
A Guider Network for Multi-Dual Learning—0
A new dog learns old tricks: RL finds classic optimization algorithms—0
Backplay: 'Man muss immer umkehren'—0
Automata Guided Skill Composition—0
Learning Heuristics for Automated Reasoning through Reinforcement Learning—0
Learning to Reinforcement Learn by Imitation—0
DHER: Hindsight Experience Replay for Dynamic GoalsCode0
Soft Q-Learning with Mutual-Information Regularization—0
SUPERVISED POLICY UPDATECode0
Rating Continuous Actions in Spatial Multi-Agent Problems—0
Predicted Variables in Programming—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
Challenges of Real-World Reinforcement LearningCode1
Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage—0
Deep Neuroevolution of Recurrent and Discrete World ModelsCode0
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Benchmark Results

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