if not os.path.exists(zoo_dir):
raise Exception('zoo directory does not exist')
# zoo directory must contain pretrained and extended models
pretrained_dir = zoo_dir / "pretrained"
extended_dir = zoo_dir / "extended"
# Example:
# reward_wrapper_dir = zoo_dir / "reward_wrapper"
# Add the test agents you would like to evaluate to the test_agents dictionary { "model_name": "path/to/model"}
# You may delete the existing ones in the test_agents dictionary (they are just examples)
test_agents = {
"baseline": BaselinePolicy(),
"ppo": PPOPolicy(pretrained_dir / "ppo.zip"),
"ppo_sp": PPOPolicy(pretrained_dir / "ppo_sp.zip"),
"ga_sp": makeSlimePolicyLite(pretrained_dir / "ga_sp.json"),
"random": RandomPolicy(),
}
if args.benchmark and not args.evaltest:
pretrained_agents = {
"baseline": BaselinePolicy(),
"ppo": PPOPolicy(pretrained_dir / "ppo.zip"),
"ppo_sp": PPOPolicy(pretrained_dir / "ppo_sp.zip"),
"ga_sp": makeSlimePolicyLite(pretrained_dir / "ga_sp.json"),
"random": RandomPolicy(),
}
extended_agents = {
"ppo_extended": PPOPolicy(extended_dir / "ppo.zip"),
"ppo_sp_extended": PPOPolicy(extended_dir / "ppo_sp.zip"),
if not os.path.exists(zoo_dir):
raise Exception('zoo directory does not exist')
# zoo directory must contain pretrained and extended models
pretrained_dir = zoo_dir / "pretrained"
extended_dir = zoo_dir / "extended"
# Example:
# reward_wrapper_dir = zoo_dir / "reward_wrapper"
obs_small_always_dir = zoo_dir / "obs_small_always"
# Add the test agents you would like to evaluate to the test_agents dictionary { "model_name": "path/to/model"}
# You may delete the existing ones in the test_agents dictionary (they are just examples)
test_agents = {
"ppo": PPOPolicy(obs_small_always_dir / "ppo.zip"),
"ppo_sp": PPOPolicy(obs_small_always_dir / "ppo_sp.zip"),
"ga_sp": makeSlimePolicyLite(obs_small_always_dir / "ga_sp.json"),
}
if args.benchmark and not args.evaltest:
pretrained_agents = {
"baseline": BaselinePolicy(),
"ppo": PPOPolicy(pretrained_dir / "ppo.zip"),
"ppo_sp": PPOPolicy(pretrained_dir / "ppo_sp.zip"),
"ga_sp": makeSlimePolicyLite(pretrained_dir / "ga_sp.json"),
"random": RandomPolicy(),
}
extended_agents = {
"ppo_extended": PPOPolicy(extended_dir / "ppo.zip"),
"ppo_sp_extended": PPOPolicy(extended_dir / "ppo_sp.zip"),
"ga_sp_extended": makeSlimePolicyLite(extended_dir / "ga_sp.json"),
pretrained_dir = zoo_dir / "pretrained"
extended_dir = zoo_dir / "extended"
# Example:
# reward_wrapper_dir = zoo_dir / "reward_wrapper"
spike_dir = zoo_dir / "spike"
arc_dir = zoo_dir / "arc"
# Add the test agents you would like to evaluate to the test_agents dictionary { "model_name": "path/to/model"}
# You may delete the existing ones in the test_agents dictionary (they are just examples)
test_agents_1 = {
"ppo_spike_best": PPOPolicy(spike_dir / "ppo_best.zip"),
"ppo_sp_spike_best": PPOPolicy(spike_dir / "ppo_sp_best.zip"),
"ppo_spike_final": PPOPolicy(spike_dir / "ppo_final.zip"),
"ppo_sp_spike_final": PPOPolicy(spike_dir / "ppo_sp_final.zip"),
"ga_sp_spike": makeSlimePolicyLite(spike_dir / "ga_sp.json"),
}
test_agents_2 = {
"ppo_arc_best": PPOPolicy(arc_dir / "ppo_best.zip"),
"ppo_sp_arc_best": PPOPolicy(arc_dir / "ppo_sp_best.zip"),
"ppo_arc_final": PPOPolicy(arc_dir / "ppo_final.zip"),
"ppo_sp_arc_final": PPOPolicy(arc_dir / "ppo_sp_final.zip"),
"ga_sp_arc": makeSlimePolicyLite(arc_dir / "ga_sp.json"),
}
if args.benchmark and not args.evaltest:
pretrained_agents = {
"baseline": BaselinePolicy(),
"ppo": PPOPolicy(pretrained_dir / "ppo.zip"),
"ppo_sp": PPOPolicy(pretrained_dir / "ppo_sp.zip"),