Revisit Agent classes

[kngwyu]

Now we have two types of agents

• OneStepAgent
  • for DQN-like algorithms
  • execute 1-step + stores transition to replay buffer + train agent by sampled transitions
• NStepParallelAgent
  • for A2C-like algorithms
  • execute N-step in parallel environments + train the policy in an online manner
    These 2 divisions are practical but lack flexibility.
    E.g., we cannot extend OneStep algorithms to batched-parallel style without rewriting the whole process.

So we should re-define agent hierarchies using some important properties, like

• Online/Offline(or use replay buffer or not)
• MultiStep/OneStep
• Not Parallel/Batch Parallel/ Async Parallel

[kngwyu]

Closed via #54