138ced23ba
* Currently this is specific to the case of discretizing a continuous action space. Can easily be adapted to other case by feeding the kNN otherwise, and removing the usage of a discretizing output action filter
2.7 KiB
Actions space: Discrete
References: Deep Reinforcement Learning in Large Discrete Action Spaces
Network Structure
Algorithm Description
Choosing an action
Pass the current states through the actor network, and get a proto action μ. While in training phase, use a continuous exploration policy, such as the a gaussian noise, to add exploration noise to the proto action. Then, pass the proto action to a k-NN tree to find actual valid action candidates, which are in the surrounding neighborhood of the proto action. Those actions are then passed to the critic to evaluate their goodness, and eventually the discrete index of the action with the highest Q value is chosen. When testing, the same flow is used, but no exploration noise is added.
Training the network
Training the network is exactly the same as in DDPG. Unlike when choosing the action, the proto action is not passed through the k-NN tree. It is being passed directly to the critic.
Start by sampling a batch of transitions from the experience replay.
To train the critic network, use the following targets:
yt = r(st, at) + γ⋅Q(st + 1, μ(st + 1))
First run the actor target network, using the next states as the inputs, and get μ(st + 1). Next, run the critic target network using the next states and μ(st + 1), and use the output to calculate yt according to the equation above. To train the network, use the current states and actions as the inputs, and yt as the targets.
To train the actor network, use the following equation:
∇θμJ ≈ Estρβ[∇aQ(s, a)|s = st, a = μ(st)⋅∇θμμ(s)|s = st]
Use the actor's online network to get the action mean values using the current states as the inputs. Then, use the critic online network in order to get the gradients of the critic output with respect to the action mean values ∇aQ(s, a)|s = st, a = μ(st). Using the chain rule, calculate the gradients of the actor's output, with respect to the actor weights, given ∇aQ(s, a). Finally, apply those gradients to the actor network.
After every training step, do a soft update of the critic and actor target networks' weights from the online networks.