agent = ppo.PPOTrainer(config)
policy = agent.get_policy()
print(policy.model) # Prints the model summary
where config looks the following:
{
"batch_mode": "truncate_episodes",
"clip_param": 0.3,
"entropy_coeff": 0.0,
"entropy_coeff_schedule": null,
"env": "CityFlows",
"env_config": {
"config_path": "examples/1x1/config.json",
"reward_func": "delay_from_opt",
"steps_per_episode": 1000
},
"evaluation_interval": 3,
"evaluation_num_episodes": 20,
"framework": "torch",
"grad_clip": null,
"kl_coeff": 0.2,
"kl_target": 0.01,
"lambda": 1.0,
"lr": 5e-05,
"lr_schedule": null,
"model": {
"_disable_action_flattening": false,
"_disable_preprocessor_api": true,
"_use_default_native_models": false,
"conv_activation": "relu",
"conv_filters": null,
"custom_model": null,
"custom_model_config": {}
},
"num_sgd_iter": 30,
"rollout_fragment_length": 200,
"seed": 123,
"sgd_minibatch_size": 128,
"shuffle_sequences": true,
"train_batch_size": 4000,
"use_critic": true,
"use_gae": true,
"vf_clip_param": 10.0,
"vf_loss_coeff": 1.0
}
With Actor: PPO
With Model: CNN
With ENV: CityFlows
(using torch as framework)
However, when I print the model I’m getting FullyConnectedNetwork.
Any Idea how the config[model] not effect the agent model?