[WIP] Refactoring RL models

[hill-a]

• refactored A2C, ACER, ACTKR, DDPG, DeepQ, GAIL, TRPO, PPO1 and PPO2 under a single constant class
• added callback to refactored algorithm training
• added saving and loading to refactored algorithms
• refactored ACER, DDPG, GAIL, PPO1 and TRPO to fit with A2C, PPO2 and ACKTR policies
• added new policies for most algorithms (Mlp, MlpLstm, MlpLnLstm, Cnn, CnnLstm and CnnLnLstm)
• added dynamic environment switching (so continual RL learning is now feasible)
• added prediction from observation and action probability from observation for all the algorithms
• fixed graphs issues, so models wont collide in names
• fixed behavior_clone weight loading for GAIL
• fixed Tensorflow using all the GPU VRAM
• fixed models so that they are all compatible with vectorized environments
• fixed set_global_seed to update gym.spaces's random seed
• fixed PPO1 and TRPO performance issues when learning identity function
• added new tests for loading, saving, continuous actions and learning the identity function
• fixed DQN wrapping for atari
• added saving and loading for Vecnormalize wrapper
• added automatic detection of action space (for the policy network)
• fixed ACER buffer with constant values assuming n_stack=4
• fixed some RL algorithms not clipping the action to be in the action_space, when using gym.spaces.Box
• refactored algorithms can take either a gym.Environment or a str (if the environment name is registered)

TODO:

• Finish refactoring HER
• Refactor ACKTR and ACER for continuous implementation