def test_agent_stats_partial_fit_and_tuple_env():
# Define train and evaluation envs
train_env = (GridWorld, None
) # tuple (constructor, kwargs) must also work in AgentStats
# Parameters
params = {"n_episodes": 500}
horizon = 20
# Run AgentStats
stats = AgentStats(DummyAgent,
train_env,
init_kwargs=params,
n_fit=4,
eval_horizon=10)
stats2 = AgentStats(DummyAgent,
train_env,
init_kwargs=params,
n_fit=4,
eval_horizon=10)
# set some writers
stats.set_writer(0, None)
stats.set_writer(3, None)
# Run partial fit
stats.partial_fit(0.1)
stats.partial_fit(0.5)
for agent in stats.fitted_agents:
assert agent.fraction_fitted == 0.6
for _ in range(2):
stats.partial_fit(0.5)
for agent in stats.fitted_agents:
assert agent.fraction_fitted == 1.0
# Run fit
stats2.fit()
# learning curves
plot_episode_rewards([stats], cumulative=True, show=False)
# compare final policies
compare_policies([stats], eval_horizon=horizon, n_sim=10, show=False)
def test_agent_stats_seeding():
sd.set_global_seed(3456)
for env in [MountainCar(), (gym_make, {'env_name': 'MountainCar-v0'})]:
agent_stats = AgentStats(RSUCBVIAgent,
env,
init_kwargs={
'n_episodes': 2,
'horizon': 10
},
n_fit=6)
agent_stats.fit()
for ii in range(2, agent_stats.n_fit):
traj1 = get_env_trajectory(agent_stats.fitted_agents[ii - 2].env,
horizon=10)
traj2 = get_env_trajectory(agent_stats.fitted_agents[ii - 1].env,
horizon=10)
traj3 = get_env_trajectory(agent_stats.fitted_agents[ii].env,
horizon=10)
assert not compare_trajectories(traj1, traj2)
assert not compare_trajectories(traj1, traj3)
assert not compare_trajectories(traj2, traj3)
n_trials=10,
timeout=None,
n_sim=5,
n_fit=2,
n_jobs=2,
sampler_method='optuna_default')
initial_n_trials = len(ppo_stats.study.trials)
# save
ppo_stats.save('ppo_stats_backup')
del ppo_stats
# load
ppo_stats = AgentStats.load('ppo_stats_backup')
# continue previous optimization, now with 5s of timeout
best_trial, data = ppo_stats.optimize_hyperparams(n_trials=10,
timeout=5,
n_sim=5,
n_fit=2,
n_jobs=2,
continue_previous=True)
print("number of initial trials = ", initial_n_trials)
print("number of trials after continuing= ", len(ppo_stats.study.trials))
print("----")
print("fitting agents after choosing hyperparams...")
ppo_stats.fit() # fit the 4 agents