# Define optimizer parameters
optimizer_parameters = policy_net.parameters()
else:
# Define optimizer parameters
optimizer_parameters = policy_net.parameters() + encoder.parameters() + forward_model.parameters() + inverse_model.parameters()
# Only use defined parameters if there is no previous output being loaded
if RUN_TO_LOAD != None:
# Initialize and load optimizer
optimizer = optim.Adam(params=optimizer_parameters, lr=LEARNING_RATE)
optimizer.load_state_dict(OPTIMIZER_STATE_DICT)
# Load replay memory
memory = ReplayMemory(REPLAY_MEMORY_SIZE)
for chunk in REPLAY_MEMORY_CHUNKS:
memory.append_storage_chunk(chunk)
else:
# Initialize optimizer
optimizer = optim.Adam(params=optimizer_parameters, lr=LEARNING_RATE)
# Initialize replay memory
memory = ReplayMemory(REPLAY_MEMORY_SIZE)
# Initialize total variables
total_frames = 0
total_episodes = FINISHED_EPISODES
total_original_rewards = TOTAL_ORIGINAL_REWARDS
total_shaped_rewards = TOTAL_SHAPED_REWARDS
total_losses = TOTAL_LOSSES
total_start_time = time.time()
# Initialize episode variables
