Feature/gymnasium (#278)

* Release/0.8.2 (#276)

* change opencv dependency to headless and upgrade to version 4 (#275)

* change opencv dependency to headless and upgrade to version 4

* revert test changes

* version 0.8.2

* switch to gymnasium and update imports

* update state and state test

* update atari and duplicate env

* delete direct atari model test

* update vector env

* support legacy pybullet env

* update multiagent pettingzoo

* fix multi-agent atari

* fix ma atari preset test

* fix single_env_experiment tests

* update parallel env experiment test

* update multiagent env experiment test

* run formatter

* remove legacy gym import

* add legacy gym dependency

* specify pybullet gym dependency correctly

* update to pong_v3 in multiagent integration test

all/agents/ddpg.py

@@ -20,7 +20,7 @@ class DDPG(Agent):
         q (QContinuous): An Approximation of the continuous action Q-function.
         policy (DeterministicPolicy): An Approximation of a deterministic policy.
         replay_buffer (ReplayBuffer): The experience replay buffer.
-        action_space (gym.spaces.Box): Description of the action space.
+        action_space (gymnasium.spaces.Box): Description of the action space.
         discount_factor (float): Discount factor for future rewards.
         minibatch_size (int): The number of experiences to sample in each training update.
         noise (float): the amount of noise to add to each action (before scaling).