def test_rnnsac_compilation(self):
"""Test whether RNNSAC can be built on all frameworks."""
config = (
sac.RNNSACConfig().rollouts(num_rollout_workers=0).training(
# Wrap with an LSTM and use a very simple base-model.
model={"max_seq_len": 20},
policy_model_config={
"use_lstm": True,
"lstm_cell_size": 64,
"fcnet_hiddens": [10],
"lstm_use_prev_action": True,
"lstm_use_prev_reward": True,
},
q_model_config={
"use_lstm": True,
"lstm_cell_size": 64,
"fcnet_hiddens": [10],
"lstm_use_prev_action": True,
"lstm_use_prev_reward": True,
},
replay_buffer_config={
"type": "MultiAgentPrioritizedReplayBuffer",
"replay_burn_in": 20,
"zero_init_states": True,
},
lr=5e-4,
))
num_iterations = 1
# Test building an RNNSAC agent in all frameworks.
for _ in framework_iterator(config, frameworks="torch"):
trainer = config.build(env="CartPole-v0")
for i in range(num_iterations):
results = trainer.train()
print(results)
check_compute_single_action(
trainer,
include_state=True,
include_prev_action_reward=True,
)
def _import_rnnsac():
from ray.rllib.algorithms import sac
return sac.RNNSAC, sac.RNNSACConfig().to_dict()