Search Results for author:
Found 2 papers, 1 papers with code
Balancing policy constraint and ensemble size in uncertainty-based offline reinforcement learning
Offline reinforcement learning agents seek optimal policies from fixed data sets.
Improving TD3-BC: Relaxed Policy Constraint for Offline Learning and Stable Online Fine-Tuning
The ability to discover optimal behaviour from fixed data sets has the potential to transfer the successes of reinforcement learning (RL) to domains where data collection is acutely problematic.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
