def check_support_multiagent(alg, config):
register_env("multi_agent_mountaincar",
lambda _: MultiAgentMountainCar({"num_agents": 2}))
register_env("multi_agent_cartpole",
lambda _: MultiAgentCartPole({"num_agents": 2}))
config["log_level"] = "ERROR"
if "DDPG" in alg:
a = get_agent_class(alg)(config=config, env="multi_agent_mountaincar")
else:
a = get_agent_class(alg)(config=config, env="multi_agent_cartpole")
try:
a.train()
finally:
a.stop()
def check_support_multiagent(alg, config):
register_env("multi_agent_mountaincar",
lambda _: MultiAgentMountainCar({"num_agents": 2}))
register_env("multi_agent_cartpole",
lambda _: MultiAgentCartPole({"num_agents": 2}))
config["log_level"] = "ERROR"
for _ in framework_iterator(config, frameworks=("torch", "tf")):
if alg in ["DDPG", "APEX_DDPG", "SAC"]:
a = get_agent_class(alg)(
config=config, env="multi_agent_mountaincar")
else:
a = get_agent_class(alg)(config=config, env="multi_agent_cartpole")
print(a.train())
a.stop()
def check_support_multiagent(alg, config):
register_env("multi_agent_mountaincar",
lambda _: MultiAgentMountainCar({"num_agents": 2}))
register_env("multi_agent_cartpole",
lambda _: MultiAgentCartPole({"num_agents": 2}))
config["log_level"] = "ERROR"
for fw in framework_iterator(config):
if fw in ["tf2", "tfe"] and \
alg in ["A3C", "APEX", "APEX_DDPG", "IMPALA"]:
continue
if alg in ["DDPG", "APEX_DDPG", "SAC"]:
a = get_trainer_class(alg)(config=config,
env="multi_agent_mountaincar")
else:
a = get_trainer_class(alg)(config=config,
env="multi_agent_cartpole")
print(a.train())
a.stop()
def check_support_multiagent(alg, config):
register_env("multi_agent_mountaincar",
lambda _: MultiAgentMountainCar({"num_agents": 2}))
register_env("multi_agent_cartpole",
lambda _: MultiAgentCartPole({"num_agents": 2}))
# Simulate a simple multi-agent setup.
policies = {
"policy_0": PolicySpec(config={"gamma": 0.99}),
"policy_1": PolicySpec(config={"gamma": 0.95}),
}
policy_ids = list(policies.keys())
def policy_mapping_fn(agent_id, episode, worker, **kwargs):
pol_id = policy_ids[agent_id]
return pol_id
config["multiagent"] = {
"policies": policies,
"policy_mapping_fn": policy_mapping_fn,
}
for fw in framework_iterator(config):
if fw in ["tf2", "tfe"] and \
alg in ["A3C", "APEX", "APEX_DDPG", "IMPALA"]:
continue
if alg in ["DDPG", "APEX_DDPG", "SAC"]:
a = get_trainer_class(alg)(config=config,
env="multi_agent_mountaincar")
else:
a = get_trainer_class(alg)(config=config,
env="multi_agent_cartpole")
results = a.train()
check_train_results(results)
print(results)
a.stop()