feat(pu/tj): add Chinese chess environment and alphazero/muzero bot training demo - #499
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- Document Xiangqi state, observation planes, action mask, rewards, termination, and battle modes - Explain bot-mode agent and random-bot transitions and MuZero stochasticity - Add clear training, checkpoint evaluation, terminal-play, and direct environment examples - Update MuZero bot-mode timestep and target settings with regression coverage
- Add a terminal example using play_with_bot_mode without a checkpoint - Document ICCS input, deterministic seeding, and the random-bot workflow - Clarify that this baseline is not an Xiangqi MCTS engine
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Summary
Design
Verification
pytest -q zoo/board_games/chinese_chess/test— 14 passed(57, 10, 9)observations and 2,086-action headsThis follows the Chinese-chess work explored in #442 and #459 with an independently implemented, compact rules core.