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 9451–9475 of 15113 papers

TitleStatusHype
Decentralized Deterministic Multi-Agent Reinforcement Learning—0
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning—0
Model-Invariant State Abstractions for Model-Based Reinforcement Learning—0
TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning—0
Sim-Env: Decoupling OpenAI Gym Environments from Simulation ModelsCode0
Privacy-Preserving Kickstarting Deep Reinforcement Learning with Privacy-Aware Learners—0
Smart Feasibility Pump: Reinforcement Learning for (Mixed) Integer Programming—0
Reinforcement Learning for Datacenter Congestion Control—0
Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems—0
Strategic bidding in freight transport using deep reinforcement learning—0
Continuous Doubly Constrained Batch Reinforcement LearningCode0
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning—0
Learning Memory-Dependent Continuous Control from Demonstrations—0
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning—0
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs—0
Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning—0
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method—0
Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents—0
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—0
Training Larger Networks for Deep Reinforcement Learning—0
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models—0
TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution—0
Transferring Domain Knowledge with an Adviser in Continuous Tasks—0
Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments—0
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Benchmark Results

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