Release 1.0.0

TD3
New APIs for Coach usage as a library
Updated Getting Started tutorial
Batch RL tutorial

Release 0.12.1

Fixes for breaking API changes (OpenAI Gym, Scipy)
OPE: Weighted Importance Sampling
Creating a dataset using an agent
Printing input size as part of network summary

Release 0.12.0

ACER
Soft Actor-Critic
BCQ
Batch RL
Off-policy evaluation (estimators: DM, DR, Sequential DR, IPS)

Release 0.11.2

Intel Tensorflow fix.

Release 0.11.1

Roll out worker memory leak fix
wxPython dependency removal

Release 0.11.0

Horizontal scaling
MxNet support
ONNX export
New documentation

Release 0.10.0

A complete redesign - non-backward compatible. Enabling multi-agent support.

New features -

• PIP package
• Benchmarks
• Hierarchical Reinforcement Learning (demonstrated by Hierarchical Actor-Critic)
• Tutorials
• Shared memory (e.g. Replay Buffer) between workers
• Tests (unit-tests, reward-based tests, trace-based tests)
• Using Coach as a library (see example here)

New Environments -

• Toy Environments (Exploration Chain, BitFlip)
• DeepMind PySC2 support (Starcraft 2)
• DeepMind Control Suite

New Algorithms -

• Hindsight Experience Replay
• Prioritized Experience Replay
• Hierarchical Actor-Critic
• UCB with Q-Ensembles

Release 0.9.0

New features -

• CARLA 0.7 simulator integration
• Human control of the game play
• Recording of human game play and storing / loading the replay buffer
• Behavioral cloning agent and presets
• Golden tests for several presets
• Selecting between deep / shallow image embedders
• Rendering through pygame (with some boost in performance)

API changes -

• Improved environment wrapper API
• Added an evaluate flag to allow convenient evaluation of existing checkpoints
• Improve frameskip definition in Gym

Bug fixes -

• Fixed loading of checkpoints for agents with more than one network
• Fixed the N Step Q learning agent python3 compatibility

v0.8.0

Initial public release