Questions tagged [actor-critic-methods]

For questions related to the family of reinforcement learning algorithms denoted by "actor-critic", where there is an actor (a policy) and a critic (a value function).

Filter by
Sorted by
Tagged with
30
votes
4answers
10k views

How should I handle invalid actions (when using REINFORCE)?

I want to create an AI which can play five-in-a-row/gomoku. I want to use reinforcement learning for this. I use policy gradient method, namely REINFORCE, with baseline. For the value and policy ...
2
votes
1answer
529 views

Why isn't my implementation of A2C for the the atari pong game converging?

I have two different implementations with PyTorch of the Atari Pong game using A2C algorithm. Both implementations are similar, but some portion are different. https://colab.research.google.com/drive/...
4
votes
1answer
185 views

What is the advantage of using more than one environment with the advantage actor-critic?

make_env = lambda: ptan.common.wrappers.wrap_dqn(gym.make("PongNoFrameskip-v4")) envs = [make_env() for _ in range(NUM_ENVS)] Here is a code you can look at. ...
13
votes
3answers
7k views

What is the difference between actor-critic and advantage actor-critic?

I'm struggling to understand the difference between actor-critic and advantage actor-critic. At least, I know they are different from asynchronous advantage actor-critic (A3C), as A3C adds an ...
3
votes
1answer
173 views

How does being on-policy prevent us from using the replay buffer with the policy gradients?

One of the approaches to improving the stability of the Policy Gradient family of methods is to use multiple environments in parallel. The reason behind this is the fundamental problem we discussed in ...
2
votes
0answers
44 views

Problems with gradient-biased actor critic methods

To my knowledge, there are at least 6 different variants of Actor Critic: \begin{array}{l l l l} \text{actor gradient} & \text{critic gradient} & \text{actor gradient biased} & \text{name} ...
1
vote
1answer
133 views

Once the environments are vectorized, how do I have to gather immediate experiences for the agent?

My main purpose right now is to train an agent using the A2C algorithm to solve the Atari Breakout game. So far I have succeeded to create that code with a single agent and environment. To break the ...
0
votes
1answer
474 views

Why is this PyTorch implementation of the actor-critic algorithm inconsistent with the mathematical formulas?

This PyTorch implementation of the actor-critic algorithm calculates the losses like so: ...