def testRolloutDictSpace(self):
register_env("nested", lambda _: NestedDictEnv())
agent = PGTrainer(env="nested")
agent.train()
path = agent.save()
agent.stop()
# Test train works on restore
agent2 = PGTrainer(env="nested")
agent2.restore(path)
agent2.train()
# Test rollout works on restore
rollout(agent2, "nested", 100)
def test_rollout_dict_space(self):
register_env("nested", lambda _: NestedDictEnv())
agent = PGTrainer(env="nested", config={"framework": "tf"})
agent.train()
path = agent.save()
agent.stop()
# Test train works on restore
agent2 = PGTrainer(env="nested", config={"framework": "tf"})
agent2.restore(path)
agent2.train()
# Test rollout works on restore
rollout(agent2, "nested", 100)
MARWIL_agent = MARWILTrainer(config=marwil_config, env=SSA_Tasker_Env)
MARWIL_agent.restore(marwil_checkpoint)
MARWIL_agent.get_policy().config['explore'] = False
pg_config = PG_CONFIG.copy()
pg_config['batch_mode'] = 'complete_episodes'
pg_config['train_batch_size'] = 2000
pg_config['lr'] = 0.0001
pg_config['evaluation_interval'] = None
pg_config['postprocess_inputs'] = True
pg_config['env_config'] = env_config
pg_config['explore'] = False
PGR_agent = PGTrainer(config=pg_config, env=SSA_Tasker_Env)
PGR_agent.restore(pgr_checkpoint)
PGR_agent.get_policy().config['explore'] = False
PGRE_agent = PGTrainer(config=pg_config, env=SSA_Tasker_Env)
PGRE_agent.restore(pgre_checkpoint)
PGRE_agent.get_policy().config['explore'] = False
OLR_agent = PGTrainer(config=pg_config, env=SSA_Tasker_Env)
OLR_agent.restore(olr_checkpoint)
OLR_agent.get_policy().config['explore'] = False
def ppo_agent(obs, env):
return PPO_agent.compute_action(obs)
