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 3101–3110 of 15113 papers

TitleStatusHype
Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning—0
Reinforcement Learning Under Probabilistic Spatio-Temporal Constraints with Time Windows—0
Dialogue Shaping: Empowering Agents through NPC Interaction—0
TrackAgent: 6D Object Tracking via Reinforcement Learning—0
Primitive Skill-based Robot Learning from Human Evaluative Feedback—0
Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture SearchCode0
ETHER: Aligning Emergent Communication for Hindsight Experience Replay—0
Approximate Model-Based Shielding for Safe Reinforcement LearningCode0
Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation—0
Actions Speak What You Want: Provably Sample-Efficient Reinforcement Learning of the Quantal Stackelberg Equilibrium from Strategic Feedbacks—0
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Benchmark Results

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