h, bSSKJr SSKrSSKrSSKJr SSKJrJ r SSK r \ RRS5 "SS\R\R\\R\45r"SS \R\R\\R\45r"S S \R\R\\R\45r"S S \R\R\\R\45r"SS\R\R\\R\45r"SS\R,5r"SS\R0\R\\R45r"SS\R\R\\R\45rg!\ a Sr GNpf=f)) SupportsFloatN)spaces)AtariResetReturnAtariStepReturnFcp^\rSrSrSrS\R S\SS4U4SjjrS\ 4Sjr S \ S\ 4S jr S rU=r$) StickyActionEnvz Sticky action. Paper: https://arxiv.org/abs/1709.06009 Official implementation: https://github.com/mgbellemare/Arcade-Learning-Environment :param env: Environment to wrap :param action_repeat_probability: Probability of repeating the last action envaction_repeat_probabilityreturnNcv>[TU]U5 X lURR 5SS:XdegNrNOOP)super__init__r unwrappedget_action_meanings)selfr r __class__s a/home/james-whalen/.local/lib/python3.13/site-packages/stable_baselines3/common/atari_wrappers.pyrStickyActionEnv.__init__s6 )B&}}00215???c HSUlURR"S0UD6$)Nr)_sticky_actionr reset)rkwargss rrStickyActionEnv.reset!s xx~~'''ractioncURR5UR:aXlURR UR5$N) np_randomrandomr rr step)rrs rr$StickyActionEnv.step%s< >> "d&D&D D"( xx}}T0011r)rr )__name__ __module__ __qualname____firstlineno____doc__gymEnvfloatrrrintrr$__static_attributes__ __classcell__rs@rrrsO@CGG@@$@ (!1(232?22rrcb^\rSrSrSrS S\R S\SS4U4SjjjrS\ 4Sjr S r U=r $) NoopResetEnv+z Sample initial states by taking random number of no-ops on reset. No-op is assumed to be action 0. :param env: Environment to wrap :param noop_max: Maximum value of no-ops to run r noop_maxr Nc>[TU]U5 X lSUlSUlUR R 5SS:Xdegr)rrr5override_num_noops noop_actionrr)rr r5rs rrNoopResetEnv.__init__4sE  "&}}00215???rc URR"S0UD6 URb URnO3URRR SUR S-5nUS:de[R"S5n0n[U5HZnURRUR5up5pgnU(d U(dM<URR"S0UD6up4M\ X44$)Nrr) r rr7rr"integersr5npzerosranger$r8)rrnoopsobsinfo_ terminated truncateds rrNoopResetEnv.reset;s    " " .++ENN,,55a9JKEqyyhhqkuA26((--@P@P2Q /CJ4YY HHNN4V4 Tyr)r8r5r7)) r&r'r(r)r*r+r,r.rrrr/r0r1s@rr3r3+sB@CGG@s@D@@ !1  rr3cZ^\rSrSrSrS\R SS4U4SjjrS\4Sjr Sr U=r $) FireResetEnvKze Take action on reset for environments that are fixed until firing. :param env: Environment to wrap r r Nc>[TU]U5 URR5SS:Xde[ URR55S:deg)Nr;FIRE)rrrrlenrr rs rrFireResetEnv.__init__RsP }}00215???3==44671<<[TU]U5 SUlSUlgNrT)rrlives was_real_donerOs rrEpisodicLifeEnv.__init__js  !rrc URRU5up#pEnU=(d UUlURRRR 5nSUs=:aUR :aO OSnXplX#XEU4$rX)r r$rZralerY)rrrArewardrDrErBrYs rr$EpisodicLifeEnv.steposr3788==3H0ZD'49""&&,,. u !tzz !J J477rc dUR(aURR"S0UD6up#OKURRS5up$pVnU(dU(aURR"S0UD6up#URRR R 5UlX#4$)a8 Calls the Gym environment reset, only when lives are exhausted. This way all states are still reachable even though lives are episodic, and the learner need not know about any of this behind-the-scenes. :param kwargs: Extra keywords passed to env.reset() call :return: the first observation of the environment rr)rZr rr$rr]rY)rrrArBrCrDrEs rrEpisodicLifeEnv.reset}s   00IC37((--2B /CJ4 Y HHNN4V4 XX''++113 yr)rYrZ)r&r'r(r)r*r+r,rr.rr$rrr/r0r1s@rrUrUbsD"CGG"" 83 8? 8!1rrUcf^\rSrSrSrS S\R S\SS4U4SjjjrS\S\ 4S jr S r U=r $) MaxAndSkipEnvz Return only every ``skip``-th frame (frameskipping) and return the max between the two last frames. :param env: Environment to wrap :param skip: Number of ``skip``-th frame The same action will be taken ``skip`` times. r skipr Nc4>[TU]U5 URRcS5eURRcS5e[ R "S/URRQ7URRS9UlX lg)Nz,No dtype specified for the observation spacez*No shape defined for the observation spacerR)dtype) rrobservation_spacergshaper=r> _obs_buffer_skip)rr rers rrMaxAndSkipEnv.__init__s $$**6f8ff6$$**6d8dd688Q$E)>)>)D)D$ESMbMbMhMhi rrcSnS=p4[UR5HnURRU5upgp4nU=(d Un XPRS- :XaX`RS'XPRS- :XaX`RS'U[ U5- nU (dM O URR SS9n XX4W4$)z Step the environment with the given action Repeat action, sum reward, and max over last observations. :param action: the action :return: observation, reward, terminated, truncated, information FrRrr;)axis)r?rkr r$rjr-max) rr total_rewardrDrEirAr^rBdone max_frames rr$MaxAndSkipEnv.steps !&& tzz"A7;xx}}V7L 4C*DJJN"&)  #JJN"&)  # E&M )Lt#$$((a(0  tCCr)rjrk)) r&r'r(r)r*r+r,r.rrr$r/r0r1s@rrcrcsGCGG3tD3D?DDrrcc^^\rSrSrSrS\R SS4U4SjjrS\S\ 4Sjr S r U=r $) ClipRewardEnvzN Clip the reward to {+1, 0, -1} by its sign. :param env: Environment to wrap r r Nc$>[TU]U5 gr!)rrrOs rrClipRewardEnv.__init__s rr^c@[R"[U55$)zA Bin reward to {+1, 0, -1} by its sign. :param reward: :return: )r=signr-)rr^s rr^ClipRewardEnv.rewardswwuV}%%rr) r&r'r(r)r*r+r,rrr-r^r/r0r1s@rrxrxs7 CGG&]&u&&rrxc ^\rSrSrSrS S\R S\S\SS4U4SjjjrS \ RS\ R4S jr S r U=r $) WarpFramez Convert to grayscale and warp frames to 84x84 (default) as done in the Nature paper and later work. :param env: Environment to wrap :param width: New frame width :param height: New frame height r widthheightr NcD>[TU]U5 X lX0l[ UR [ R5(dSUR 35e[ R"SSURURS4UR RS9Ulg)NzExpected Box space, got rr;)lowhighrirg) rrrr isinstancerhrBoxrg)rr rrrs rrWarpFrame.__init__s   #//<<p@XY\YnYnXo>pp>ZQ4Z  r)rrhr)Tr)r&r'r(r)r*r+r,r.rr=ndarrayrr/r0r1s@rrrsR  CGG  C  c  4    ! ! ! !rrct^\rSrSrSrSS\R S\S\S\S\S\S \ S S 4U4S jjjr S r U=r $) AtariWrapperar Atari 2600 preprocessings Specifically: * Noop reset: obtain initial state by taking random number of no-ops on reset. * Frame skipping: 4 by default * Max-pooling: most recent two observations * Termination signal when a life is lost. * Resize to a square image: 84x84 by default * Grayscale observation * Clip reward to {-1, 0, 1} * Sticky actions: disabled by default See https://danieltakeshi.github.io/2016/11/25/frame-skipping-and-preprocessing-for-deep-q-networks-on-atari-2600-games/ for a visual explanation. .. warning:: Use this wrapper only with Atari v4 without frame skip: ``env_id = "*NoFrameskip-v4"``. :param env: Environment to wrap :param noop_max: Max number of no-ops :param frame_skip: Frequency at which the agent experiences the game. This correspond to repeating the action ``frame_skip`` times. :param screen_size: Resize Atari frame :param terminal_on_life_loss: If True, then step() returns done=True whenever a life is lost. :param clip_reward: If True (default), the reward is clip to {-1, 0, 1} depending on its sign. :param action_repeat_probability: Probability of repeating the last action r r5 frame_skip screen_sizeterminal_on_life_loss clip_rewardr r Nc0>US:a [X5nUS:a [XS9nUS:a [XS9nU(a [U5nSURR 5;a [ U5n[XUS9nU(a [U5n[TU])U5 g)Nrnr)r5r;)rerL)rr) rr3rcrUrrrIrrxrr) rr r5rrrrr rs rrAtariWrapper.__init__s %s *!#AC a<s6C >5C !#&C S]]668 8s#C{C $C rr)rGrvrTTrn) r&r'r(r)r*r+r,r.boolr-rr/r0r1s@rrrsyB&* +. WW    $ $) rr)typingr gymnasiumr+numpyr=r%stable_baselines3.common.type_aliasesrrrocl setUseOpenCL ImportErrorWrapperrr.rr3rIrUrc RewardWrapperrxObservationWrapperrrrrrrsg SGG 2ckk"**c2::s"BC243;;rzz3 C?@@3;;rzz3 C?@.0ckk"**c2::s"BC0f*DCKK CS @A*DZ&C%%&(!!&&rzz3 'BC!!H83;;rzz3 C?@8[ CsF""F.-F.