def eval(agent:Agent, env: Checkers, color:str, n_games=100):
agent.net.eval()
opponent = Agent(gamma=agent.gamma,
epsilon=1,
lr=0,
input_dims=[8*8 + 1],
batch_size=agent.batch_size,
action_space=agent.action_space,
eps_dec=0,
max_mem_size=0
)
opponent.net.eval()
initial_state = env.save_state()
score = {'black': 0, 'white': 0}
for i in tqdm(range(n_games)):
env.restore_state(initial_state)
winner = None
moves = torch.tensor(env.legal_moves())
board, turn, last_moved_piece = env.save_state()
brain = agent if turn == color else opponent
board_tensor = torch.from_numpy(env.flat_board()).view(-1).float()
encoded_turn = torch.tensor([1.]) if turn == 'black' else torch.tensor([0.])
observation = torch.cat([board_tensor, encoded_turn])
while not winner:
action = brain.choose_action(observation)
while not action_is_legal(action, moves):
action = brain.choose_action(observation)
new_board, new_turn, _, moves, winner = env.move(*action.tolist())
moves = torch.tensor(moves)
board_tensor = torch.from_numpy(env.flat_board()).view(-1).float()
encoded_turn = torch.tensor([1. if turn == 'black' else 0.])
observation = torch.cat([board_tensor, encoded_turn])
brain = agent if turn == color else opponent
score[winner] +=1
agent.net.train()
return score[color] / n_games
lr=args.lr,
eps_dec=args.epsilon_decay)
}
env = Checkers()
initial_state = env.save_state()
eps_history = []
score = {'black': 0, 'white': 0}
os.makedirs(args.checkpoints_dir, exist_ok=True)
for i in range(args.games):
print(
f"episode={i}, score={score}, black_eps:{players['black'].epsilon}, white_eps:{players['white'].epsilon}"
)
score = {'black': 0, 'white': 0}
env.restore_state(initial_state)
winner = None
moves = torch.tensor(env.legal_moves())
board, turn, last_moved_piece = env.save_state()
brain = players[turn]
board_tensor = torch.from_numpy(env.flat_board()).view(-1).float()
encoded_turn = torch.tensor([1.]) if turn == 'black' else torch.tensor(
[0.])
observation = torch.cat([board_tensor, encoded_turn])
while not winner:
action = brain.choose_action(observation)
if not action_is_legal(action, moves):
reward = -1000000
new_turn = turn
else:
new_board, new_turn, _, moves, winner = env.move(
