Understanding the NumStepsToLookAhead parameter in rlDQNAgentOptions (DQN-based reinforcement learning)
8 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
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!
1 commentaire
Réponses (1)
Aditya
le 19 Fév 2024
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.
1 commentaire
Dingshan Sun
le 19 Fév 2024
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?
Voir également
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!