Offline RL (Batch RL) is reinforcement learning from a fixed dataset of previously collected interactions — learning a policy entirely from logged data without any additional environment interaction, enabling RL in domains where online exploration is costly, dangerous, or impossible.

Offline RL Challenges

• Distribution Shift: The learned policy may visit state-action pairs not in the dataset — Q-values for unseen actions are unreliable.
• Overestimation: Standard Q-learning maximizes over poorly estimated out-of-distribution actions — catastrophic overestimation.
• Conservative Methods: CQL, IQL, TD3+BC constrain the policy to stay near the data — pessimistic value estimation.
• Dataset Quality: Performance is bounded by the quality and coverage of the offline dataset.

Why It Matters

• Safety: No online exploration needed — critical for autonomous driving, healthcare, semiconductor process control.
• Data Reuse: Leverage existing logged data (process logs, historical experiments) — no new experiments needed.
• Semiconductor: Train control policies from historical process data without risking production equipment.

Offline RL is learning from logs, not from life — training RL policies entirely from fixed datasets without environment interaction.