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74db141d5e
* SAC algorithm * SAC - updates to agent (learn_from_batch), sac_head and sac_q_head to fix problem in gradient calculation. Now SAC agents is able to train. gym_environment - fixing an error in access to gym.spaces * Soft Actor Critic - code cleanup * code cleanup * V-head initialization fix * SAC benchmarks * SAC Documentation * typo fix * documentation fixes * documentation and version update * README typo
DDPG
Each experiment uses 3 seeds and is trained for 2M environment steps. The parameters used for DDPG are the same parameters as described in the original paper.
Inverted Pendulum DDPG - single worker
coach -p Mujoco_DDPG -lvl inverted_pendulum
Inverted Double Pendulum DDPG - single worker
coach -p Mujoco_DDPG -lvl inverted_double_pendulum
Reacher DDPG - single worker
coach -p Mujoco_DDPG -lvl reacher
Hopper DDPG - single worker
coach -p Mujoco_DDPG -lvl hopper
Half Cheetah DDPG - single worker
coach -p Mujoco_DDPG -lvl half_cheetah
Walker 2D DDPG - single worker
coach -p Mujoco_DDPG -lvl walker2d
Ant DDPG - single worker
coach -p Mujoco_DDPG -lvl ant
Swimmer DDPG - single worker
coach -p Mujoco_DDPG -lvl swimmer
Humanoid DDPG - single worker
coach -p Mujoco_DDPG -lvl humanoid
