Why custom ringbuffer used instead of deque?

https://github.com/matthiasplappert/keras-rl/blob/d9e3b64a20f056030c02bfe217085b7e54098e48/rl/memory.py#L42 states: Do not use deque to implement the memory. This data structure may seem convenient but it is way too slow on random access. Instead, we use our own ring buffer implementation.

Not sure why this is stated. I ran this quick test: ` ring = RingBuffer(maxlen = 1000)

start_time = time.time()
for i in range(100000):
    ring.append(i)
print("--- %s seconds ---" % (time.time() - start_time))

d = deque(maxlen=1000)
start_time = time.time()
for i in range(100000):
    d.append(i)
print("--- %s seconds ---" % (time.time() - start_time))

start_time = time.time()
for i in range(100000):
    l = ring[i%1000-1]
print("--- %s seconds ---" % (time.time() - start_time))

start_time = time.time()
for i in range(100000):
    l = d[i%1000]
print("--- %s seconds ---" % (time.time() - start_time))

and got this result: — 0.0608789920807 seconds — — 0.00712609291077 seconds — — 0.0430929660797 seconds — — 0.00617289543152 seconds — `

You can see that the custom ringbuffer is significantly slower than the deque at adding or removing. Can someone explain to me what the comment is referring to exactly and why the ringbuffer is used?

[kirkscheper]

All my changes are here: https://github.com/kirkscheper/keras-rl/tree/recurrent-ddpg

I have been playing around with lots of different things that are probably not directly ready for a pull request.

[matthiasplappert]

Cool, feel free to update the code to use deque. I’m not sure anymore why I thought deque was too slow but it seems like this was clearly not true.