• JIT-enabled LayerNormLSTM, it is much faster than the raw implementation !

• Sync examples/mdn and examples/VAE to the latest API design

• Added instruction about how to train dm_control environments with minimal modifications.

• Remove AtariPreprocessing because it's merged as a PR in gym officially.

• Improve scripts & CI: use conda as much as possible for MKL optimization (like numpy/scipy) etc.

Research-friendly (easy to read & modify) RL baselines

It contains following algorithms for now:

• ES: CEM/CMA-ES/OpenAI-ES
• RL: VPG/PPO/DDPG/TD3/SAC

Much easier and cleaner API

Major high-level designs and major APIs converge to be stable in this release. Most of modules are well-tested.

This is a preview release of lagom. For this version, see Basics in README for quick start or directly play around with examples. A full documentation is available online at http://lagom.readthedocs.io/.