def test_agent_stats_2():
# Define train and evaluation envs
train_env = GridWorld()
eval_env = GridWorld()
# Parameters
params = {"n_episodes": 500}
# Run AgentStats
stats_agent1 = AgentStats(DummyAgent,
train_env,
eval_env=eval_env,
init_kwargs=params,
n_fit=4,
eval_horizon=10,
n_jobs=1)
stats_agent2 = AgentStats(DummyAgent,
train_env,
eval_env=eval_env,
init_kwargs=params,
n_fit=4,
eval_horizon=10,
n_jobs=1)
agent_stats_list = [stats_agent1, stats_agent2]
# set some writers
stats_agent1.set_writer(1, None)
stats_agent1.set_writer(2, None)
# compare final policies
compare_policies(agent_stats_list, n_sim=10, show=False)
compare_policies(agent_stats_list,
n_sim=10,
show=False,
stationary_policy=False)
# learning curves
plot_episode_rewards(agent_stats_list, cumulative=True, show=False)
# check if fitted
for agent_stats in agent_stats_list:
assert len(agent_stats.fitted_agents) == 4
for agent in agent_stats.fitted_agents:
assert agent.fitted
# test saving/loading
dirname = stats_agent1.output_dir
fname = dirname / 'stats'
stats_agent1.save()
loaded_stats = AgentStats.load(fname)
assert stats_agent1.identifier == loaded_stats.identifier
# delete file
os.remove(fname.with_suffix('.pickle'))
dirname.rmdir()
# test hyperparemeter optimization
loaded_stats.optimize_hyperparams()
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)
GAMMA = 0.99
HORIZON = 50
params_ppo = {
"n_episodes": N_EPISODES,
"gamma": GAMMA,
"horizon": HORIZON,
"learning_rate": 0.0003
}
# -----------------------------
# Run AgentStats
# -----------------------------
ppo_stats = AgentStats(PPOAgent, train_env, init_kwargs=params_ppo, n_fit=4)
ppo_stats.set_writer(0, SummaryWriter, writer_kwargs={'comment': 'worker_0'})
ppo_stats.set_writer(1, SummaryWriter, writer_kwargs={'comment': 'worker_1'})
agent_stats_list = [ppo_stats]
agent_stats_list[0].fit()
agent_stats_list[0].save(
) # after fit, writers are set to None to avoid pickle problems.
# learning curves
plot_episode_rewards(agent_stats_list, cumulative=True, show=False)
# compare final policies
output = compare_policies(agent_stats_list,
eval_env,
eval_horizon=HORIZON,