Questions tagged [actor-critic]
The actor-critic tag has no usage guidance.
22
questions
1
vote
0answers
29 views
A2C learning very slowly when I try to make it learn on batches as compared to making it learn on each step
I tried this on openai gym environment - LunarLander-v2.
I wrote two algorithms with just one difference:
Made it learn on each step.
Made it learn at the end of each episode.
There is a ...
2
votes
0answers
143 views
Pytorch XLA to solve the spawn problems in a Colab Env
As reference only, here is my code
It seems that torch.multiprocessing.set_start_method("spawn") can't be used in an Colab Env. Only 'fork' is allowed.
I have ...
0
votes
0answers
15 views
Action selection in actor-critic algorithm:
I have an action space that is just a list of values given by acts = [i for i in range(10, 100, 10)].
According to pytorch documentary, the loss is calculated as below. Could someone explain to me how ...
2
votes
0answers
32 views
Actions taken by agentn/ agent performance not improving
Hi I am trying to develop an rl agent using PPO algorithm. My agent takes an action(CFM) to maintain a state variable called RAT in between 24 to 24.5. I am using PPO algorithm of stable-baselines ...
1
vote
0answers
20 views
Rewards are converged but with a lot of variations
I am training a reinforcement learning agent on an episodic task of fixed episode length. I am tracking the training process by plotting the cumulative rewards over an episode. I am using tensorboard ...
1
vote
1answer
55 views
Having a reward structure which gives high positive rewards compared to the negative rewards
I am training an RL agent using PPO algorithm for a control problem. The objective of the agent is to maintain temperature in a room. It is an episodic task with episode length of 9 hrs and step size(...
1
vote
1answer
34 views
Formulation of a reward structure
I am new to reinforcement learning and experimenting with training of RL agents.
I have a doubt about reward formulation, from a given state if a agent takes a good action i give a positive reward, ...
1
vote
0answers
211 views
Actor Critic Model implementation
I am going to work on a project which requires implementation of A2C model using Tensorflow 2.0. I am new in the Machine Learning field and also in Python. These are topics which I have covered ...
0
votes
1answer
43 views
How to handle differences between training and deploying of an RL agent
Hi I am training an RL agent for a control problem. The objective of the agent is to maintain temperature in a zone. It is an episodic task with episode length of 10 hrs and actions being taken every ...
0
votes
1answer
152 views
Different results every time I train a reinforcement learning agent
I am training an RL agent for a control problem using PPO algorithm. I am using stable-baselines library for it.
The objective of an agent is to maintain a temperature of 24 deg in a zone and it ...
3
votes
1answer
2k views
Evaluating a trained Reinforcement Learning Agent?
I am new to reinforcement learning agent training. I have read about PPO algorithm and used stable baselines library to train an agent using PPO. So my question here is how do I evaluate a trained RL ...
1
vote
2answers
1k views
Agent always takes a same action in DQN - Reinforcement Learning
I have trained an RL agent using DQN algorithm. After 20000 episodes my rewards are converged. Now when I test this agent, the agent is always taking the same action , irrespective of state. I find ...
0
votes
1answer
38 views
Reward is converging but actions taken by trained agent are illogical in reinforcement learning
I am training a reinforcement learning agent using DQN. My state space has 6 variables and the agent can one action which is discretized into 500 actions
My reward structure looks like
...
1
vote
1answer
35 views
Why can't Policy Gradient Algorithm be seen as an Actor-Critic Method?
During the equation deducing in policy gradient algorithm(e.g., REINFORCE), we are actually using an expectancy of total reward, which we try to maximize.
$$\overline{R_\theta}=E_{\tau\sim\pi_\theta}[...
1
vote
1answer
546 views
A2C Continuous for Pendulum-v0 working implementation, negation for loss and entropy calculation
very good implementation of A2C continuous for Pendulum-v0
Code has snippet to stop execution when mean of last 10 or 20 is higher than -20 but the results look like:
...
1
vote
1answer
353 views
multipying negated gradients by actions for the loss in actor nn of DDPG
In this Udacity project code that I have been combing through line by line to understand the implementation, I have stumbled on a part in class Actor where this ...
1
vote
0answers
40 views
Reinforcement learning - generating a matrix of continuous values with varying size for test data generation
Currently, I am using RL A3C algorithm for test data generation, where for a set of 30 functions written in C (mostly basic algorithms like Prime number checks, triangle validity, etc.) I try to ...
0
votes
2answers
57 views
Proof subtracting baseline doesn't influence gradient can be used to show no gradient exist at all?
I am using David Silver's course in RL to help me write my thesis. However, I am baffled by the proof given in lecture 7 slide 29:
slideshow
\begin{align}
\mathbb{E}_{\pi_\theta}[\nabla_\theta \log_\...
1
vote
1answer
81 views
Stability of value function approximation in policy gradients
In DQNs, function approximation of the Q-values is unstable for correlated updates. In policy gradients with a baseline, will the value function of the policy not be plagued by the same correlated ...
3
votes
1answer
271 views
Time horizon T in policy gradients (actor-critic)
I am currently going through the Berkeley lectures on Reinforcement Learning. Specifically, I am at slide 5 of this lecture.
At the bottom of that slide, the gradient of the expected sum of rewards ...
2
votes
2answers
604 views
A3C - Turning action probabilities into intensities
I'm experimenting with using an A3C network to learn to play old Atari video games. My network outputs a set of probabilities for each possible action (e.g. left, right, shoot), and I use this ...
3
votes
1answer
884 views
How to design two different neural nets for actor and critic RL?
In order to have an actor critic RL model there are two things to be satisfied .
Value approximation function should converge to a local minimum
$$\sum_s d^{\pi}(s) \sum_a \pi(s,a)[Q^{\pi}(s,a) - ...