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 13651–13700 of 15113 papers

TitleStatusHype
Challenges in High-dimensional Reinforcement Learning with Evolution StrategiesCode0
BindsNET: A machine learning-oriented spiking neural networks library in PythonCode0
Exploration in Structured Reinforcement Learning—0
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization—0
Internal Model from Observations for Reward Shaping—0
DAQN: Deep Auto-encoder and Q-Network—0
Deep Pepper: Expert Iteration based Chess agent in the Reinforcement Learning Setting—0
Efficient Entropy for Policy Gradient with Multidimensional Action Space—0
Deep Reinforcement Learning of Region Proposal Networks for Object DetectionCode0
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition—0
GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning—0
Environment Upgrade Reinforcement Learning for Non-Differentiable Multi-Stage Pipelines—0
SeedNet: Automatic Seed Generation With Deep Reinforcement Learning for Robust Interactive Segmentation—0
Quality Signals in Generated Stories—0
Mining Evidences for Concept Stock Recommendation—0
Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning—0
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient—0
Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing—0
Integrating Episodic Memory into a Reinforcement Learning Agent using Reservoir Sampling—0
Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning—0
Equivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning—0
A Reinforcement Learning Approach to Age of Information in Multi-User Networks—0
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image TranslationCode0
Sequential Attacks on Agents for Long-Term Adversarial Goals—0
Reinforced Continual LearningCode0
Sample-Efficient Deep Reinforcement Learning via Episodic Backward UpdateCode0
Evaluating Reinforcement Learning Algorithms in Observational Health Settings—0
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning—0
Adversarial Learning of Task-Oriented Neural Dialog Models—0
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics ModelsCode2
Depth and nonlinearity induce implicit exploration for RL—0
Observe and Look Further: Achieving Consistent Performance on Atari—0
Supervised Policy Update for Deep Reinforcement LearningCode0
Virtuously Safe Reinforcement Learning—0
Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning—0
Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition—0
Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement LearningCode0
Hierarchical clustering with deep Q-learning—0
Importance Weighted Transfer of Samples in Reinforcement Learning—0
Reward Constrained Policy OptimizationCode1
Value Propagation Networks—0
Memory Augmented Self-PlayCode0
Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement LearningCode0
Fingerprint Policy Optimisation for Robust Reinforcement Learning—0
Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation—0
Fast Policy Learning through Imitation and Reinforcement—0
Detecting Deceptive Reviews using Generative Adversarial Networks—0
A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions—0
Finite Sample Analysis of LSTD with Random Projections and Eligibility Traces—0
Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological RewardsCode0
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Benchmark Results

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