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TD3

Set the parameters in block "Set the parameters" in TD3_Ant.ipynb:

  • env_name = Name of enviroment
  • seed = Random seed
  • start_timesteps = When to start training TD3 model
  • eval_freq = Frequency of evaluation
  • max_timesteps = Maximum timesteps
  • save_models = Need save model ?
  • expl_noise = Exploration noise
  • batch_size = Batch size
  • discount = Discount factor
  • tau = The parameter to smoothly update targrt network in TD3 paper
  • policy_noise = Policy noise to do target policy smoothing
  • noise_clip = Maximum value of policy noise
  • policy_freq = Frequency to update the actor and target networks

Model Training:

After setting up all parameters, just click the button "Run All", then you can train TD3 model and evaluate it. You can find the model file in the folder "./pytorch_models" and reward records of evaluations in "./results/[enviroment name]".

Results:

  • DP: Delayed Policy updates
  • TPS: Target Policy Smoothing
  • CDQ: Clipped Double Q-learning