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

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Showing 2451–2475 of 15113 papers

TitleStatusHype
A Multiagent CyberBattleSim for RL Cyber Operation Agents—0
Continual State Representation Learning for Reinforcement Learning using Generative Replay—0
AMRL: Aggregated Memory For Reinforcement Learning—0
Backstepping Temporal Difference Learning—0
2048: Reinforcement Learning in a Delayed Reward Environment—0
Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot—0
A Comparative Study of Deep Reinforcement Learning for Crop Production Management—0
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs—0
Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity—0
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning—0
Adaptive Reward-Poisoning Attacks against Reinforcement Learning—0
Continual Reinforcement Learning with Complex Synapses—0
A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications—0
Backplay: 'Man muss immer umkehren'—0
Scene Induced Multi-Modal Trajectory Forecasting via Planning—0
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction—0
Continual Vision-based Reinforcement Learning with Group Symmetries—0
Continuous Control for Automated Lane Change Behavior Based on Deep Deterministic Policy Gradient Algorithm—0
Continuous Motion Planning with Temporal Logic Specifications using Deep Neural Networks—0
Control-Aware Representations for Model-based Reinforcement Learning—0
BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning—0
PolicyCleanse: Backdoor Detection and Mitigation in Reinforcement Learning—0
Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches—0
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Benchmark Results

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