(
self, state_dim, action_dim, action_range, hidden_dim, replay_buffer, policy_target_update_interval=1,
q_lr=3e-4, policy_lr=3e-4
)
| 210 | class TD3: |
| 211 | |
| 212 | def __init__( |
| 213 | self, state_dim, action_dim, action_range, hidden_dim, replay_buffer, policy_target_update_interval=1, |
| 214 | q_lr=3e-4, policy_lr=3e-4 |
| 215 | ): |
| 216 | self.replay_buffer = replay_buffer |
| 217 | |
| 218 | # initialize all networks |
| 219 | self.q_net1 = QNetwork(state_dim, action_dim, hidden_dim) |
| 220 | self.q_net2 = QNetwork(state_dim, action_dim, hidden_dim) |
| 221 | self.target_q_net1 = QNetwork(state_dim, action_dim, hidden_dim) |
| 222 | self.target_q_net2 = QNetwork(state_dim, action_dim, hidden_dim) |
| 223 | self.policy_net = PolicyNetwork(state_dim, action_dim, hidden_dim, action_range) |
| 224 | self.target_policy_net = PolicyNetwork(state_dim, action_dim, hidden_dim, action_range) |
| 225 | print('Q Network (1,2): ', self.q_net1) |
| 226 | print('Policy Network: ', self.policy_net) |
| 227 | |
| 228 | # initialize weights of target networks |
| 229 | self.target_q_net1 = self.target_ini(self.q_net1, self.target_q_net1) |
| 230 | self.target_q_net2 = self.target_ini(self.q_net2, self.target_q_net2) |
| 231 | self.target_policy_net = self.target_ini(self.policy_net, self.target_policy_net) |
| 232 | |
| 233 | # set train mode |
| 234 | self.q_net1.train() |
| 235 | self.q_net2.train() |
| 236 | self.target_q_net1.eval() |
| 237 | self.target_q_net2.eval() |
| 238 | self.policy_net.train() |
| 239 | self.target_policy_net.eval() |
| 240 | |
| 241 | self.update_cnt = 0 |
| 242 | self.policy_target_update_interval = policy_target_update_interval |
| 243 | |
| 244 | self.q_optimizer1 = tf.optimizers.Adam(q_lr) |
| 245 | self.q_optimizer2 = tf.optimizers.Adam(q_lr) |
| 246 | self.policy_optimizer = tf.optimizers.Adam(policy_lr) |
| 247 | |
| 248 | def target_ini(self, net, target_net): |
| 249 | """ hard-copy update for initializing target networks """ |
no test coverage detected