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You could use Ray RLlib. It has support for parallel environments, even over multiple GPUs and compute nodes.


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We don't need multiple environments. On-policy algorithms require that new training samples are collected with the newest policy, so we can't use an experience buffer. However we can use the newest policy to collect multiple samples, even over multiple epochs, before updating the weights. This update can be a batch update.


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In toy problems like the Short Corridor task, you can choose the state representation to explore a key property, such as the ability of a particular method to solve it. Often this is done to extremes and heavily simplified. That is what is going on here. The state space that the agent is allowed to use is made highly degenerate with respect to the problem. ...


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You can choose those states, but is the agent aware of the state it is in? From the text, it seems that the agent cannot distinguish between the three states. Its observation function is completely uninformative. This is why a stochastic policy is what is needed. This is common for POMDPs, whereas for regular MDPs we can always find a deterministic policy ...


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I think this question is hinting at the problem of choosing an exploration strategy. The simplest strategy is to use the so called epsilon-greedy strategy (or $\epsilon$-greedy). This means that you select an action at random $x$ percent of the times that an agent has to select an action. The other times, the agent takes the action that its current policy ...


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