Implement CQL

Key Insight

CQL (Conservative Q-Learning) fixes the out-of-distribution blow-up of naive offline RL with one extra penalty bolted onto the normal Q-learning loss: it pushes down the predicted value of all the actions the network is tempted to overrate, while pulling up the value of the actions actually present in the dataset. The net effect is pessimism — the learned Q deliberately underestimates unfamiliar actions, so the policy stops chasing the fantasy high-value actions that wrecked naive Q-learning and stays near the behavior policy's data. It is the value-pessimism counterpart to keeping the policy close to the data (policy constraint), and reproducing its D4RL numbers is the standard way to learn the approach.