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 3451–3460 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning with Embedded LQR Controllers—0
DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local & Global Collision Avoidance—0
AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning—0
Deep Model Compression Via Two-Stage Deep Reinforcement Learning—0
Counterfactual Explanation Policies in RL—0
A Strong Baseline for Batch Imitation Learning—0
Auto-Encoding Adversarial Imitation Learning—0
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—0
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy—0
Counterfactual Credit Assignment in Model-Free Reinforcement Learning—0
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Benchmark Results

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