REINFORCE Recurrent CartPole-v0
Name: reinforce_recurrent_cartpole
Date completed:
Description: Baseline experiment
Hypotheses: N/A
Prerequisites: N/A
Algorithms: REINFORCE with Recurrent Network
Environments: CartPole-v0
Specs:
{
"reinforce_recurrent_cartpole": {
"agent": [{
"name": "Reinforce",
"algorithm": {
"name": "Reinforce",
"action_pdtype": "default",
"action_policy": "default",
"action_policy_update": "no_update",
"explore_var_start": null,
"explore_var_end": null,
"explore_anneal_epi": null,
"gamma": 0.99,
"add_entropy": false,
"entropy_weight": 0.01,
"continuous_action_clip": 2.0,
"training_frequency": 1
},
"memory": {
"name": "OnPolicyNStepReplay"
},
"net": {
"type": "RecurrentNet",
"hid_layers": [32],
"hid_layers_activation": "relu",
"num_rnn_layers": 1,
"seq_len": 4,
"clip_grad": false,
"clip_grad_val": 1.0,
"loss_spec": {
"name": "MSELoss"
},
"optim_spec": {
"name": "Adam",
"lr": 0.01
},
"lr_decay": "rate_decay",
"lr_decay_frequency": 500,
"lr_decay_min_timestep": 1000,
"gpu": true
}
}],
"env": [{
"name": "CartPole-v0",
"max_timestep": null,
"max_episode": 1000
}],
"body": {
"product": "outer",
"num": 1
},
"meta": {
"max_session": 4,
"max_trial": 100,
"search": "RandomSearch",
"max_generation": null
},
"search": {
"agent": [{
"net": {
"hid_layers__choice": [
[16],
[64],
[32, 16],
[64, 32]
],
"hid_layers_activation__choice": ["sigmoid", "relu", "tanh"],
"optim_spec": {
"lr__uniform": [0.0001, 0.2]
}
}
}]
}
}
}
Running instructions: use the spec above
Commit: 454f620fce3e6fe2f87a91bffd74667d1f8a94f9
Results summary: