This example includes the implementations of the following reinforcement learning algorithms:

• ES
  • Cross Entropy Method (CEM)
  • Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
  • OpenAI-ES
• RL
  • Vanilla Policy Gradient (VPG)
  • Proximal Policy Optimization (PPO)
  • Deep Deterministic Policy Gradients (DDPG)
  • Twin Delayed DDPG (TD3)
  • Soft Actor-Critic (SAC)

• How to train with dm_control environments?
  • Modify experiment.py: use dm2gym wrapper, e.g.

  from gym.wrappers import FlattenDictWrapper
  from dm_control import suite
  from dm2gym import DMControlEnv
  
  config = Config(
      ...
      'env.id': Grid([('cheetah', 'run'), ('hopper', 'hop'), ('walker', 'run'), ('fish', 'upright')]),
      ...
      )
  
  def make_env(config, seed):
      domain_name, task_name = config['env.id']
      env = suite.load(domain_name, task_name, environment_kwargs=dict(flat_observation=True))
      env = DMControlEnv(env)
      env = FlattenDictWrapper(env, ['observations'])
      ...