def my_train_fn(config, reporter):
iterations = config.pop("train-iterations", 10)
# Train for n iterations with high LR
agent1 = PPO(env="CartPole-v0", config=config)
for _ in range(iterations):
result = agent1.train()
result["phase"] = 1
reporter(**result)
phase1_time = result["timesteps_total"]
state = agent1.save()
agent1.stop()
# Train for n iterations with low LR
config["lr"] = 0.0001
agent2 = PPO(env="CartPole-v0", config=config)
agent2.restore(state)
for _ in range(iterations):
result = agent2.train()
result["phase"] = 2
result["timesteps_total"] += phase1_time # keep time moving forward
reporter(**result)
agent2.stop()
print(f".. best checkpoint was: {best_checkpoint}")
# Create a new dummy Trainer to "fix" our checkpoint.
new_trainer = PPO(config=config)
# Get untrained weights for all policies.
untrained_weights = new_trainer.get_weights()
# Restore all policies from checkpoint.
new_trainer.restore(best_checkpoint)
# Set back all weights (except for 1st agent) to original
# untrained weights.
new_trainer.set_weights(
{pid: w
for pid, w in untrained_weights.items() if pid != "policy_0"})
# Create the checkpoint from which tune can pick up the
# experiment.
new_checkpoint = new_trainer.save()
new_trainer.stop()
print(".. checkpoint to restore from (all policies reset, "
f"except policy_0): {new_checkpoint}")
print("Starting new tune.run")
# Start our actual experiment.
stop = {
"episode_reward_mean": args.stop_reward,
"timesteps_total": args.stop_timesteps,
"training_iteration": args.stop_iters,
}
# Make sure, the non-1st policies are not updated anymore.
config["multiagent"]["policies_to_train"] = [
print(f".. best checkpoint was: {best_checkpoint}")
# Create a new dummy Algorithm to "fix" our checkpoint.
new_algo = PPO(config=config)
# Get untrained weights for all policies.
untrained_weights = new_algo.get_weights()
# Restore all policies from checkpoint.
new_algo.restore(best_checkpoint)
# Set back all weights (except for 1st agent) to original
# untrained weights.
new_algo.set_weights(
{pid: w
for pid, w in untrained_weights.items() if pid != "policy_0"})
# Create the checkpoint from which tune can pick up the
# experiment.
new_checkpoint = new_algo.save()
new_algo.stop()
print(".. checkpoint to restore from (all policies reset, "
f"except policy_0): {new_checkpoint}")
print("Starting new tune.run")
# Start our actual experiment.
stop = {
"episode_reward_mean": args.stop_reward,
"timesteps_total": args.stop_timesteps,
"training_iteration": args.stop_iters,
}
# Make sure, the non-1st policies are not updated anymore.
config["multiagent"]["policies_to_train"] = [