Understanding the NumStepsToLookAhead parameter in rlDQNAgentOptions (DQN-based reinforcement learning)

Hi,

I have a brief question with regard to DQN-based reinforcement learning, in particular with regard to the rlDQNAgentOptions parameter "NumStepsToLookAhead".

Considering that DQN is an off-policy method where training is performed on a minibatch of experiences (s,a,r,s') which are not "in episodic order", how can you implement a n-step return? (That's what I think "NumStepsToLookAhead>1" results in.)

Thank you so much for your help!

[Dingshan Sun]

I have the same doubt. Can anyone give a hint?

Answer 1

[Aditya]

In Deep Q-Networks (DQN), the `NumStepsToLookAhead` parameter in `rlDQNAgentOptions` indeed refers to the use of n-step returns during the training process. While DQN is typically associated with 1-step returns, using n-step returns can sometimes stabilize training and lead to better performance.

Here's how n-step returns can be implemented in an off-policy method like DQN:

1. Experience Replay Buffer: The agent's experiences are stored in an experience replay buffer (also known as a replay memory). Each experience typically consists of a tuple `(s, a, r, s')`, where `s` is the current state, `a` is the action taken, `r` is the reward received, and `s'` is the next state.

2. N-step Return Calculation: When `NumStepsToLookAhead` is set to a value greater than 1, the agent computes the n-step return for each experience in the minibatch. This means that instead of using the immediate reward `r`, the agent looks ahead `n` steps into the future and accumulates rewards over those steps to form the n-step return. This is done by summing the discounted rewards over the next `n` steps and then adding the discounted estimated Q-value of the state-action pair at the nth step.

3. Off-policy Correction: Since DQN is an off-policy algorithm, it can update its Q-values based on experiences that are not in the order they were collected. For n-step returns, the agent still samples experiences randomly from the replay buffer. However, for each sampled experience, it looks ahead `n` steps in the buffer to calculate the n-step return. The off-policy nature of DQN means that these n-step transitions do not need to be from the same episode or contiguous in time.

4. Target Calculation: The target for the Q-value update is then calculated using the n-step return. The target Q-value for the state-action pair `(s, a)` is the sum of the discounted rewards for the next `n` steps plus the discounted Q-value of the state-action pair at the nth step, as estimated by the target network.

[Dingshan Sun]

Thank you for answering. But it still a little confusing to me that, why the off-policy nature of DQN allows that the n-step transitions do not need to be from the same episode. Let's have a look at the DQN algorithm and how the values are updated:

The n-step rewards should be included in R_i, is that right? Then how is it possible that the n-step transitions can be from different episodes or no contiguous in time?