def main(cfg):
env_name, use_zf, gamma, tau, policy_state, filter_state =\
cfg.require("env name", "use zfilter", "advantage gamma", "advantage tau", "policy state dict", "filter state dict")
logger = Logger()
logger.init(cfg)
filter_op = ZFilter(gamma, tau, enable=use_zf)
env = FakeGym(env_name)
policy = Policy(cfg, env.info())
agent = Agent(cfg, env, policy, filter_op)
# ---- start training ---- #
if policy_state is not None:
agent.policy().reset(policy_state)
if filter_state is not None:
agent.filter().reset(filter_state)
train_loop(cfg, agent, logger)
print("Done")
def main(cfg):
env_name, action_mode, policy_state, filter_state =\
cfg.require("env name", "action mode", "policy state dict", "filter state dict")
logger = Logger()
logger.init(cfg)
filter_op = Filter()
# env = FakeGym(env_name)
env = FakeRLBench(env_name, action_mode=action_mode)
policy = Policy(cfg, env.info())
agent = Agent(cfg, env, policy, filter_op)
# ---- start training ---- #
if policy_state is not None:
agent.policy().reset(policy_state)
if filter_state is not None:
agent.filter().reset(filter_state)
train_loop(cfg, agent, logger)
print("Done")
