max_memory_size=MAX_MEMORY_SIZE,
num_episodes=NUM_EPISODES,
replace_after=REPLACE_AFTER,
use_per=USE_PER,
per_a=1)
# Filling memory
print("Filling replay memory")
state = env.reset()
if USE_TB:
writer.add_graph(agent.Q_loc, torch.FloatTensor([state]).to(device))
for t in range(200):
action = env.action_space.sample()
next_state, reward, done, info = env.step(action)
agent.store_transition(state, action, reward, next_state, done)
state = next_state
if done:
state = env.reset()
print("Finished filling memory")
# Learning
print("Started learning")
rewards = []
time_learned = []
n_episode = 100
time_per_n_episode = datetime.now()
total_cleared = 0
alpha=ALPHA,
max_memory_size=MAX_MEMORY_SIZE,
num_episodes=NUM_EPISODES,
replace_after=REPLACE_AFTER,
use_per=USE_PER)
# Filling memory
print("Filling replay memory")
state = env.reset()
if USE_TB:
writer.add_graph(agent.Q_loc,
torch.FloatTensor([downsample(state)]).to(device))
for t in range(2000):
action = env.action_space.sample()
next_state, reward, done, info = env.step(action)
agent.store_transition(downsample(state), action, reward,
downsample(next_state), done)
state = next_state
if done:
state = env.reset()
print("Finished filling memory")
# Learning
print("Started learning")
rewards = []
time_learned = []
n_episode = 100
time_per_n_episode = datetime.now()
for i_episode in range(NUM_EPISODES):
stepcount = 0
state = env.reset()