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inrlcode/reinforcement-learning (press backspace or delete to remove)

I was here: https://github.com/rlcode/reinforcement-learning/tree/master/2-cartpole After the first image, the rest look like this: image Code for those looks like this: image In the repo, the file ...
  • EngrStudent
  • Opened 
    on Jul 8, 2024
  • #121

I am running the script here but even after 500 episodes it does not converge. You can see the graph I get below: score In contrast this is the supposedly converged graph from repo: cartpole_reinforce ...
  • a-ozbek
  • Opened 
    on Aug 18, 2021
  • #108

Hello, I saw there was run.py script for running the example in README. But I can t find that script. How can I get full-code example? Thanks, Regards.
  • ghost
  • Opened 
    on Apr 7, 2021
  • #105

Hey all! I get the following issue running, reinforce_agent.py and all of under, 3-atari. Prior to this, this same lines caused, Tensor to Numpy array issue which was resolved by adding, from tensorflow.python.framework.ops ...
  • ShakthiYasas
  • 1
  • Opened 
    on Apr 2, 2021
  • #104

Any idea how to go about implementing diagonal movement in grid score?
  • karlstraube
  • Opened 
    on Sep 24, 2020
  • #99

I want to run other atari game, it s performance looks doesn t good. Could anyone help me? Whether I can achieve this goal by change gym.make( ENV_NAME ) , and it s real_action? help me plllllz, appreciate ...
  • THSWind
  • Opened 
    on Jul 27, 2020
  • #98

Hi, I would like to test some hyperparameters, with using threading, that will be much faster. But when I run threading on DQN and DDQN algorithm, the error says: Tensor Tensor( dense_1/kernel:0 , shape=(2, ...
  • Ayanamii-i
  • Opened 
    on May 25, 2020
  • #96

i was looking at the code for breakout and i saw various saved models ,but the code is only for one saved model then how the other models were saved, i want to know if they were saved after making some ...
  • chetanya230598
  • Opened 
    on Apr 15, 2020
  • #95

You are doing Q-Learning: # get action for the current state and go one step in environment action = agent.get_action(state) next_state, reward, done, info = env.step(action) ...
  • laz8
  • 1
  • Opened 
    on Mar 19, 2020
  • #94
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