General Notes: (1). Entire codebase is in DQN_Implementation.py file. (2). This script only takes the environment name as input from command line. (3). All other parameters are set within the script at the time of initializing the DQN_Agent() class.
Q.1.
Running Linear QN for CartPole and no experience replay: On line 371 set "self.model_type='linear_dqn'" On line 372 set "self.train_type='no_replay_memory'" python DQN_Implementation.py --env=CartPole-v0
Running Linear DQN for MountainCar and no experience replay: On line 375 set "self.train_type='no_replay_memory'" On line 376 set "self.model_type='linear_dqn'" python DQN_Implementation.py --env=MountainCar-v0
Whenever replay experience is not used. We do the training of network in a stochastic fashion (one sample at a time, no mini-batch).
Q.2.
Running Linear QN for CartPole and with experience replay: On line 371 set "self.model_type='linear_dqn'" On line 372 set "self.train_type='use_replay_memory'" python DQN_Implementation.py --env=CartPole-v0
Running Linear DQN for MountainCar and with experience replay: On line 375 set "self.train_type='use_replay_memory'" On line 376 set "self.model_type='linear_dqn'" python DQN_Implementation.py --env=MountainCar-v0
Wheneve replay experience is use. We do the training of network at mini-batch level. Batch size is kept as 32 always.
Q.3.
Running Deep Q-Network for CartPole with no experience replay: On line 371 set "self.model_type='dqn'" On line 372 set "self.train_type='no_replay_memory'" python DQN_Implementation.py --env=CartPole-v0
Running Deep Q-Network for CartPole and with experience replay: On line 371 set "self.model_type='dqn'" On line 372 set "self.train_type='use_replay_memory'" python DQN_Implementation.py --env=CartPole-v0
Running Deep Q-Network for MountainCar and no experience replay: On line 375 set "self.train_type='no_replay_memory'" On line 376 set "self.model_type='dqn'" python DQN_Implementation.py --env=MountainCar-v0
Running Deep Q-Network for MountainCar and no experience replay: On line 375 set "self.train_type='use_replay_memory'" On line 376 set "self.model_type='dqn'" python DQN_Implementation.py --env=MountainCar-v0
Wheneve replay experience is use. We do the training of network at mini-batch level. Batch size is kept as 32 always. Whenever replay experience is not used. We do the training of network in a stochastic fashion (one sample at a time, no mini-batch).
Q.4.
Running Dueling DQN for CartPole with no experience replay: On line 371 set "self.model_type='ddqn'" On line 372 set "self.train_type='no_replay_memory'" python DQN_Implementation.py --env=CartPole-v0
Running Dueling DQN for CartPole and with experience replay: On line 371 set "self.model_type='ddqn'" On line 372 set "self.train_type='use_replay_memory'" python DQN_Implementation.py --env=CartPole-v0
Running Dueling DQN for MountainCar and no experience replay: On line 375 set "self.train_type='no_replay_memory'" On line 376 set "self.model_type='ddqn'" python DQN_Implementation.py --env=MountainCar-v0
Running Dueling DQN for MountainCar and no experience replay: On line 375 set "self.train_type='use_replay_memory'" On line 376 set "self.model_type='ddqn'" python DQN_Implementation.py --env=MountainCar-v0
Wheneve replay experience is use. We do the training of network at mini-batch level. Batch size is kept as 32 always. Whenever replay experience is not used. We do the training of network in a stochastic fashion (one sample at a time, no mini-batch).
Q.5.
Running image based DQN for SpaceInvades with experience replay: python DQN_Implementation.py --env=SpaceInvaders
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent