แนวคิดหลัก
Value Explicit Pretraining (VEP) proposes a method to learn generalizable representations for transfer reinforcement learning by pre-training an encoder using self-supervised contrastive loss. The approach enables improved performance in unseen tasks.
บทคัดย่อ
Value Explicit Pretraining (VEP) introduces a method to learn transferable representations for reinforcement learning, outperforming current methods on Atari and visual navigation tasks. By leveraging Monte Carlo estimates of control heuristics, VEP achieves significant improvements in rewards and sample efficiency.
The content discusses the challenges in visual representation learning and the importance of discovering correct inductive biases. Various pretraining methods are compared, highlighting the need for approaches that encode information useful for downstream control during representation learning.
VEP utilizes offline demonstration datasets with reward labels to learn representations based on similar Monte Carlo Bellman return estimates across tasks. This allows for efficient policy learning and transfer to new tasks with related goals.
Experiments on Atari games and visual navigation benchmarks demonstrate the effectiveness of VEP, showcasing superior performance compared to state-of-the-art methods. The method's implementation details, including contrastive representation learning and discounted returns, are thoroughly explained.
Overall, VEP presents a promising approach to address the challenge of transferring policies to novel but related tasks efficiently through learned representations based on value estimates.
สถิติ
VEP achieves up to a 2× improvement in rewards on Atari games and visual navigation.
VEP shows up to a 3× improvement in sample efficiency.
The method is evaluated using expert videos from training tasks without fine-tuning.
Performance gains are observed both on in-domain tasks and transfer experiments.
Baseline methods like VIP and TCN are compared against VEP.
คำพูด
"VEP learns generalizable representations by pre-training an encoder using self-supervised contrastive loss."
"Observations with similar Monte Carlo Bellman return estimates share similar success propensities."
"VEP outperforms baselines by improving policy learning and aiding transfer to new tasks."