def test_agent_solve_bit_flipping_game():
AGENTS = [PPO, DDQN, DQN_With_Fixed_Q_Targets, DDQN_With_Prioritised_Experience_Replay, DQN, DQN_HER]
trainer = Trainer(config, AGENTS)
results = trainer.train()
for agent in AGENTS:
agent_results = results[agent.agent_name]
agent_results = np.max(agent_results[0][1][50:])
assert agent_results >= 0.0, "Failed for {} -- score {}".format(agent.agent_name, agent_results)
def test_agents_can_play_games_of_different_dimensions():
config.num_episodes_to_run = 10
config.hyperparameters["DQN_Agents"]["batch_size"] = 3
AGENTS = [A2C, A3C, PPO, DDQN, DQN_With_Fixed_Q_Targets, DDQN_With_Prioritised_Experience_Replay, DQN]
trainer = Trainer(config, AGENTS)
config.environment = gym.make("CartPole-v0")
results = trainer.train()
for agent in AGENTS:
assert agent.agent_name in results.keys()
AGENTS = [SAC, TD3, PPO, DDPG]
config.environment = gym.make("MountainCarContinuous-v0")
trainer = Trainer(config, AGENTS)
results = trainer.train()
for agent in AGENTS:
assert agent.agent_name in results.keys()
AGENTS = [DDQN, SNN_HRL]
config.environment = Four_Rooms_Environment(15, 15, stochastic_actions_probability=0.25,
random_start_user_place=True, random_goal_place=False)
trainer = Trainer(config, AGENTS)
results = trainer.train()
for agent in AGENTS:
assert agent.agent_name in results.keys()
"batch_size": 128,
"buffer_size": 100000,
"epsilon": 1.0,
"epsilon_decay_rate_denominator": 150,
"discount_rate": 0.999,
"alpha_prioritised_replay": 0.6,
"beta_prioritised_replay": 0.1,
"incremental_td_error": 1e-8,
"update_every_n_steps": 15,
"tau": 1e-2,
"linear_hidden_units": [256, 256],
"final_layer_activation": "softmax",
# "y_range": (-1, 14),
"batch_norm": False,
"gradient_clipping_norm": 5,
"HER_sample_proportion": 0.8,
"learning_iterations": 1,
"clip_rewards": False
}
}
config.model = FCNN()
if __name__== '__main__':
AGENTS = [DQN, DRQN, ]#DDQN, Dueling_DDQN, DDQN_With_Prioritised_Experience_Replay]
trainer = Trainer(config, AGENTS)
trainer.train()
