def train_and_assert(self, agent_type, is_v1: bool, num_iterations=100):
logger = logging.warning
v2_backends = [b for b in get_backends(agent_type, skip_v1=True)]
v1_backends = [
b for b in get_backends(agent_type) if (not b in v2_backends)
]
backends = v1_backends if is_v1 else v2_backends
for backend in backends:
logger(
f'backend={backend} agent={agent_type}, num_iterations={num_iterations}'
)
cem_agent: CemAgent = agent_type('CartPole-v0',
fc_layers=(100, ),
backend=backend)
tc: core.TrainContext = cem_agent.train(
[log.Duration(),
log.Iteration(eval_only=True),
log.Agent()],
num_iterations=num_iterations,
num_iterations_between_eval=10,
max_steps_per_episode=200,
default_plots=False)
(min_steps, avg_steps,
max_steps) = tc.eval_steps[tc.episodes_done_in_training]
assert max_steps >= 100
assert avg_steps >= 50
def test_train(self):
for backend in get_backends(SacAgent):
sac_agent: SacAgent = SacAgent('CartPole-v0', backend=backend)
sac_agent.train([log.Duration(), log.Iteration(), log.Agent()],
num_iterations=10,
max_steps_per_episode=200,
default_plots=False)
def test_train(self):
for backend in get_backends(SacAgent):
sac_agent: SacAgent = SacAgent(_mountaincart_continuous_name, backend=backend)
tc: core.TrainContext = sac_agent.train([log.Duration(), log.Iteration(eval_only=True), duration.Fast()],
default_plots=False)
r = max_avg_rewards(tc)
assert r >= -1
def test_train(self):
for backend in get_backends(RandomAgent):
reinforce_agent: ReinforceAgent = ReinforceAgent('CartPole-v0', backend=backend)
tc: core.TrainContext = reinforce_agent.train([log.Duration(), log.Iteration()],
num_iterations=10,
max_steps_per_episode=200,
default_plots=False)
(min_steps, avg_steps, max_steps) = tc.eval_steps[tc.episodes_done_in_training]
assert avg_steps >= 10
def test_train(self):
for backend in get_backends(RandomAgent):
reinforce_agent: ReinforceAgent = ReinforceAgent(_line_world_name, backend=backend)
tc: core.TrainContext = reinforce_agent.train([log.Duration(), log.Iteration()],
num_iterations=10,
max_steps_per_episode=200,
default_plots=False)
r = max_avg_rewards(tc)
assert r >= 5
def train_and_eval(self, agent_type, backend, num_iterations):
dqn_agent: DqnAgent = agent_type(_cartpole_name, fc_layers=(100,), backend=backend)
tc: core.TrainContext = dqn_agent.train([log.Duration(), log.Iteration(eval_only=True), log.Agent()],
num_iterations=num_iterations,
num_steps_buffer_preload=1000,
num_iterations_between_eval=500,
max_steps_per_episode=200,
default_plots=False)
return max_avg_rewards(tc)
def train_and_eval(self, agent_type, backend, num_iterations):
dqn_agent: DqnAgent = agent_type('CartPole-v0', fc_layers=(100,), backend=backend)
tc: core.TrainContext = dqn_agent.train([log.Duration(), log.Iteration(eval_only=True), log.Agent()],
num_iterations=num_iterations,
num_steps_buffer_preload=1000,
num_iterations_between_eval=500,
max_steps_per_episode=200,
default_plots=False)
max_avg_steps = max([avg_steps for (min_steps, avg_steps, max_steps) in tc.eval_steps.values()])
return max_avg_steps
