![]() ![]() Takes in agent and returns the observation space for that agent.ĭefault implementation is to return the observation_spaces dict observe ( agent ) # Or any other resources that should be released. MUST return the same value for the same agent nameĭefault implementation is to return the action_spaces dict close ( ) #Ĭloses any resources that should be released.Ĭloses the rendering window, subprocesses, network connections, Takes in agent and returns the action space for that agent. raw_env ( render_mode = None ) # action_space ( agent ) # V1: Bumped version of all environments due to adoption of new agent iteration scheme where all agents are iterated over after they are done (1.4.0)ĪPI # class. V2: Legal action mask in observation replaced illegal move list in infos (1.5.0) V3: Fixed bug in arbitrary calls to observe() (1.8.0) V4: Changed observation space to proper AlphaZero style frame stacking (1.11.0) V5: Changed python-chess version to version 1.7 (1.13.1) Taking an illegal move ends the game with a reward of -1 for the illegally moving agent and a reward of 0 for all other agents. The action_mask will be all zeros for any agent except the one The action_mask is a binary vector where each index of the vector represents whether the action is legal or not. The legal moves available to the current agent are found in the action_mask element of the dictionary observation. This can be accomplished using the frame_stacking argument of our wrapper. Unlike AlphaZero, the observation space does not stack the observations previous moves by default. In other words, the two players are looking at mirror images of the board, not the same board. Like AlphaZero, the board is always oriented towards the current agent (the currant agent’s king starts on the 1st row). Possibilities are represented by displaying the vulnerable pawn on the 8th row instead of the 5th.Ĭhannel 19: represents whether a position has been seen before (whether a position is a 2-fold repetition) An index of this channel is set to 1 if a black knight is in the corresponding spot on the game board, otherwise, it is set to 0. For example, there is a specific channel that represents black knights. Represented by a single channel where the n th element in the flattened channel is set if there has been n movesĬhannel 6: All ones to help neural networks find board edges in padded convolutionsĬhannel 7 - 18: One channel for each piece type and player color combination. It has 20 channels representing:Ĭhannel 0: All ones if white can castle queensideĬhannel 1: All ones if white can castle kingsideĬhannel 2: All ones if black can castle queensideĬhannel 3: All ones if black can castle kingsideĬhannel 5: A move clock counting up to the 50 move rule. Like AlphaZero, the main observation space is an 8x8 image representing the board. The observation is a dictionary which contains an 'observation' element which is the usual RL observation described below, and an 'action_mask' which holds the legal moves, described in the Legal Actions Mask section. Our implementation of the observation and action spaces for chess are what the AlphaZero method uses, with two small changes. Chess is one of the oldest studied games in AI. ![]()
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