)
net_config = dict(
hidden_units=(256, 256),
activation=torch.nn.Tanh
)
eg = ExperimentGrid(name=experiment_name)
eg.add('env_name', env_config['env_name'], '', False)
# eg.add('seed', 0)
# eg.add('resume', '/home/c2/src/tmp/spinningup/data/intersection_2_agents_fine_tune_add_left_yield2/intersection_2_agents_fine_tune_add_left_yield2_s0_2020_03-23_22-40.11')
# eg.add('reinitialize_optimizer_on_resume', True)
# eg.add('num_inputs_to_add', 0)
# eg.add('pi_lr', 3e-6)
# eg.add('vf_lr', 1e-5)
# eg.add('boost_explore', 5)
eg.add('epochs', 20000)
eg.add('steps_per_epoch', 4000)
eg.add('ac_kwargs:hidden_sizes', net_config['hidden_units'], 'hid')
eg.add('ac_kwargs:activation', net_config['activation'], '')
eg.add('notes', notes, '')
eg.add('run_filename', os.path.realpath(__file__), '')
eg.add('env_config', env_config, '')
def train():
eg.run(ppo_pytorch)
if __name__ == '__main__':
utils.run(train_fn=train, env_config=env_config, net_config=net_config)
physics_steps_per_observation=12,
discrete_actions=COMFORTABLE_ACTIONS,
)
net_config = dict(hidden_units=(64, 64), activation=torch.nn.Tanh)
eg = ExperimentGrid(name=experiment_name)
eg.add('env_name', env_config['env_name'], '', False)
pso = env_config['physics_steps_per_observation']
effective_horizon_seconds = 10
eg.add(
'gamma', 1 - pso / (effective_horizon_seconds * FPS)
) # Lower gamma so seconds of effective horizon remains at 10s with current physics steps = 12 * 1/60s * 1 / (1-gamma)
eg.add('epochs', 10000)
eg.add('steps_per_epoch', 8000)
eg.add('ac_kwargs:hidden_sizes', net_config['hidden_units'], 'hid')
eg.add('ac_kwargs:activation', net_config['activation'], '')
eg.add('notes', notes, '')
eg.add('run_filename', os.path.realpath(__file__), '')
eg.add('env_config', env_config, '')
def train():
eg.run(ppo_pytorch)
if __name__ == '__main__':
utils.run(train_fn=train,
env_config=env_config,
net_config=net_config,
num_eval_episodes=10)
effective_horizon_seconds = 10
eg.add(
'gamma', 1 - pso / (effective_horizon_seconds * FPS)
) # Lower gamma so seconds of effective horizon remains at 10s with current physics steps = 12 * 1/60s * 1 / (1-gamma)
eg.add('epochs', 417)
eg.add('steps_per_epoch', 20000)
eg.add('ac_kwargs:hidden_sizes', net_config['hidden_units'], 'hid')
eg.add('ac_kwargs:activation', net_config['activation'], '')
eg.add('notes', notes, '')
eg.add('run_filename', os.path.realpath(__file__), '')
eg.add('env_config', env_config, '')
def train():
eg.run(ppo_pytorch)
if __name__ == '__main__':
utils.run(train_fn=train,
env_config=env_config,
net_config=net_config,
try_rollouts=20,
steps_per_try_rollout=10,
num_eval_episodes=10)
# | VALUE | REWARD
# 2 rollouts 10 steps | 7.71 | 7.78
# 2 rollouts 20 steps | 7.72 | 7.65
# 2 rollouts 30 steps | 7.52 |
# 4 rollouts 10 steps | 7.75 |
# 20 rollouts 10 steps | | 7.55!
