Concetti Chiave
The key factor impacting the performance of offline reinforcement learning algorithms on diverse data is the scale of the network architecture.
Sintesi
The content discusses challenges faced by offline reinforcement learning algorithms when dealing with diverse datasets. It introduces a new testbed called MOOD to illustrate these challenges and proposes hypotheses to explain the performance drop. The importance of network scale in improving performance is highlighted, along with other design considerations such as evaluation sampling and advantage sampling. Results from empirical evaluations on both MOOD and D4RL benchmarks are presented, showing that larger network architectures significantly improve algorithm performance.
Introduction:
- Offline RL promises training agents exclusively on logged data.
- Policy constrained methods aim to keep learned policy close to data distribution.
- Existing methods struggle with extrapolation beyond given data.
Offline RL Algorithms:
- TD3+BC adds behavioral cloning term for policy improvement.
- AWAC maximizes likelihood weighted by exponentiated advantage function.
- IQL learns value function using expectile regression.
MOOD Testbed:
- MOOD based on DeepMind Control suite highlights impact of data diversity.
- Mixed-objective datasets show significant performance drop for existing offline RL methods.
Hypotheses and Solutions:
- Over-conservatism: Policies forced to stay close to data distribution may lead to suboptimal actions.
- Network Scale: Larger architectures improve performance by modeling wider state-action spaces.
- Epistemic Uncertainty: Overestimation bias in Q-value estimates can hinder evaluation performance.
- Bias and Variance: Advantage-weighted algorithms introduce bias and variance, affecting algorithm performance.
Empirical Results:
- Large network architectures significantly bridge the performance gap with same-objective datasets.
- Evaluation sampling contributes to improvements in some tasks but has a detrimental effect in others.
- ASAC performs on par with AWAC, indicating variance is not a limiting factor.
Statistiche
"Surprisingly, we find that scale emerges as the key factor impacting performance."
"We show similar positive results in the canonical D4RL benchmark."
"ASAC’s performance is on par with AWAC, indicating that the variance of the AWAC estimator is not a limiting factor."
Citazioni
"Adding data from various sources significantly reduces the performance of all considered offline RL algorithms."
"Large modern architectures surpass state-of-the-art performance."
"All algorithms use deeper architectures for the actor compared to the critic."