ObsKey = str
ActionKey = str
:param obs: Dict[ObsKey, torch.Tensor]
:return: Dict[ActionKey, np.ndarray]
"""
pass
def act_eval(self, obs):
"""
Generate an action in an evaluation.
ObsKey = str
ActionKey = str
:param obs: Dict[ObsKey, torch.Tensor]
:return: Dict[ActionKey, np.ndarray]
"""
pass
if __name__ == "__main__":
args = parse_args()
agent_reg = AgentRegistry()
agent_reg.register_agent(MyCustomAgent)
main(args, agent_registry=agent_reg)
# Call script like this to train agent:
# python -m custom_agent_stub.py --agent MyCustomAgent
"""
pass
def reset(self, **kwargs):
"""
Reset environment.
ObsKey = str
:param kwargs:
:return: Dict[ObsKey, Any] Observation dictionary
"""
pass
def close(self):
"""
Close any connections / resources.
:return:
"""
pass
if __name__ == "__main__":
env_reg = EnvRegistry()
env_reg.register_env(MyCustomEnv, ["scenario1", "scenario2"])
main(parse_args(), env_registry=env_reg)
# Call script like this to train agent:
# python -m custom_env_stub.py --env scenario1
def new_internals(self, device):
"""
Define any initial hidden states here, move them to device if necessary.
InternalKey=str
:return: Dict[InternalKey, torch.Tensor (ND)]
"""
pass
def forward(self, observation, internals):
"""
Compute forward pass.
ObsKey = str
InternalKey = str
:param observation: Dict[ObsKey, torch.Tensor (1D | 2D | 3D | 4D)]
:param internals: Dict[InternalKey, torch.Tensor (ND)]
:return: torch.Tensor
"""
pass
if __name__ == "__main__":
args = parse_args()
network_reg = NetworkRegistry()
network_reg.register_network(MyCustomNetwork)
main(args, net_registry=network_reg)
# Call script like this to train agent:
# python -m custom_network_stub.py --custom-network MyCustomNetwork
pass
def reset(self, **kwargs):
"""
Reset environment.
ObsKey = str
:param kwargs:
:return: Dict[ObsKey, Any] Observation dictionary
"""
pass
def close(self):
"""
Close any connections / resources.
:return:
"""
pass
if __name__ == "__main__":
import adept
adept.register_env(MyCustomEnv)
main(parse_args())
# Call script like this to train agent:
# python -m custom_env_stub.py --env scenario1