All Questions
5 questions
1vote
1answer
87views
Deep RL problem: Loss decreases but agent doesn't learn
I'm implementing a basic Vanilla Policy Gradient algorithm for the CartPole-v1 gymnasium environment, and I don't know what I'm doing wrong. No matter what I try, during the training loop the loss ...
- 11
1vote
1answer
603views
What is the problem in my implementation of actor critic?
I have been implementing both REINFORCE with baseline and actor-critic to solve "cartpole-v1". As a reminder, here is the presentation of the algorithms in Sutton and Barto's book (http://...
- 121
1vote
1answer
2kviews
DDPG doesn't converge for MountainCarContinuous-v0 gym environment
I am trying to implement Deep Deterministic policy gradient algorithm by referring to the paper Continuous Control using Deep Reinforcement Learning on the MountainCarContinuous-v0 gym environment. I ...
- 125
1vote
0answers
391views
Subtracting the entropy from our policy gradient will prevent our agent from being stuck in the local minimum?
In the information theory, the entropy is a measure of uncertainty in some system. Being applied to agent policy, entropy shows how much the agent is uncertain about which action to make. In math ...
- 261
3votes
0answers
44views
Reinforcement Learning on quantum circuit
I am trying to teach an agent to make any random 1-qubit state reach uniform superposition. So basically, the full circuit will be ...
