PEARL (probabilistic embeddings for actor-critic RL)
PEARL is a framework in reinforcement learning that utilizes probabilistic embeddings to enhance performance in actor-critic models, focusing on sample efficiency.
sample efficiency
Sample-efficient reinforcement learning
Sample-efficient reinforcement learning focuses on reducing the amount of data needed for training AI agents to perform specific tasks effectively.