StepNeverStop/unstable_baselines

Unstable Baselines(USB) is designed to provide a quick-start guide for Reinforcement Learning beginners and a codebase for agile algorithm development. The algorithms strictly follows the original implementations, and the performance of Unstable Baselines matches that in the original implementations. USB is currently maintained by researchers from lamda-rl.

• Baselines
  1. Deep Q Learning (DQN)
  2. Vanilla Policy Gradient (VPG)
  3. Deep Deterministic Policy Gradient (DDPG)
  4. Proximal Policy Optimization (PPO)
  5. Soft Actor Critic (SAC)
  6. Twin Delayed Deep Deterministic policy gradient algorithm (TD3)
  7. Randomized Ensembled Double Q-Learning (REDQ)
• Model Based Reinforcement Learning
  1. Model-based Policy Optimization (MBPO)
• Meta Reinforcement Learning
  1. Efficient Off-policy Meta-learning via Probabilistic Context Variables (PEARL)

• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (MAML)
• The Option-Critic Architecture (OC)

git clone --recurse-submodules https://github.com/x35f/unstable_baselines.git
cd unstable_baselines
conda env create -f env.yaml 
conda activate rl_base
pip install -e .

python3 /path/to/algorithm/main.py /path/to/algorithm/configs/some-config.json args(optional)

#install metaworld for meta_rl benchmark
cd envs/metaworld
pip install -e .
#install atari
pip install gym[all]

• Add comments for algorithms
• Add Documentation